



from pickle import UnpicklingError

import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict

from ..utils import logging


lowercase_													=      logging.get_logger(__name__)






def        __lowerCAmelCase		(	__lowerCamelCase						:     int					, __lowerCamelCase						:     Any						)	->    Any:
					try:
										with open(__lowerCamelCase					, """rb"""						) as flax_state_f:
															__lowerCAmelCase						 =from_bytes(__lowerCamelCase					, flax_state_f.read()						)
					except UnpicklingError as e:
										try:
															with open(__lowerCamelCase						) as f:
																				if f.read().startswith("""version"""						):
																									raise OSError(
																									    """You seem to have cloned a repository without having git-lfs installed. Please"""
																									    """ install git-lfs and run `git lfs install` followed by `git lfs pull` in the"""
																									    """ folder you cloned."""						)
																				else:
																									raise ValueError from e
										except (UnicodeDecodeError, ValueError):
															raise EnvironmentError(f"""Unable to convert {model_file} to Flax deserializable object. """						)

					return load_flax_weights_in_pytorch_model(__lowerCamelCase					, __lowerCamelCase						)






def        __lowerCAmelCase		(	__lowerCamelCase						:     List[Any]					, __lowerCamelCase						:     List[Any]						)	->    Optional[Any]:

					try:
										import torch  # noqa: F401
					except ImportError:
										logger.error(
										    """Loading Flax weights in PyTorch requires both PyTorch and Flax to be installed. Please see"""
										    """ https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation"""
										    """ instructions."""						)
										raise

					# check if we have bf16 weights
					__lowerCAmelCase						 =flatten_dict(jax.tree_util.tree_map(lambda __lowerCamelCase						: x.dtype == jnp.bfloataa					, __lowerCamelCase						)						).values()
					if any(__lowerCamelCase						):
										# convert all weights to fp32 if they are bf16 since torch.from_numpy can-not handle bf16

										# and bf16 is not fully supported in PT yet.
										logger.warning(
										    """Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` """
										    """before loading those in PyTorch model."""						)
										__lowerCAmelCase						 =jax.tree_util.tree_map(
										    lambda __lowerCamelCase						: params.astype(np.floataa						) if params.dtype == jnp.bfloataa else params					, __lowerCamelCase						)

					__lowerCAmelCase						 =""""""

					__lowerCAmelCase						 =flatten_dict(__lowerCamelCase					, sep="""."""						)
					__lowerCAmelCase						 =pt_model.state_dict()

					# keep track of unexpected & missing keys
					__lowerCAmelCase						 =[]
					__lowerCAmelCase						 =set(pt_model_dict.keys()						)

					for flax_key_tuple, flax_tensor in flax_state_dict.items():
										__lowerCAmelCase						 =flax_key_tuple.split("""."""						)

										if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
															__lowerCAmelCase						 =flax_key_tuple_array[:-1] + ["""weight"""]
															__lowerCAmelCase						 =jnp.transpose(__lowerCamelCase					, (3, 2, 0, 1)						)
										elif flax_key_tuple_array[-1] == "kernel":
															__lowerCAmelCase						 =flax_key_tuple_array[:-1] + ["""weight"""]
															__lowerCAmelCase						 =flax_tensor.T
										elif flax_key_tuple_array[-1] == "scale":
															__lowerCAmelCase						 =flax_key_tuple_array[:-1] + ["""weight"""]

										if "time_embedding" not in flax_key_tuple_array:
															for i, flax_key_tuple_string in enumerate(__lowerCamelCase						):
																				__lowerCAmelCase						 =(
																				    flax_key_tuple_string.replace("""_0"""					, """.0"""						)
																				    .replace("""_1"""					, """.1"""						)
																				    .replace("""_2"""					, """.2"""						)
																				    .replace("""_3"""					, """.3"""						)
																				    .replace("""_4"""					, """.4"""						)
																				    .replace("""_5"""					, """.5"""						)
																				    .replace("""_6"""					, """.6"""						)
																				    .replace("""_7"""					, """.7"""						)
																				    .replace("""_8"""					, """.8"""						)
																				    .replace("""_9"""					, """.9"""						)
																				)

										__lowerCAmelCase						 =""".""".join(__lowerCamelCase						)

										if flax_key in pt_model_dict:
															if flax_tensor.shape != pt_model_dict[flax_key].shape:
																				raise ValueError(
																				    f"""Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected """
																				    f"""to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}."""						)
															else:
																				# add weight to pytorch dict
																				__lowerCAmelCase						 =np.asarray(__lowerCamelCase						) if not isinstance(__lowerCamelCase					, np.ndarray						) else flax_tensor
																				__lowerCAmelCase						 =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
					__lowerCAmelCase						 =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)."""						)
					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."""						)

					return pt_model









import pytest


lowercase_													=      '''__dummy_dataset1__'''

lowercase_													=      '''
import json
import os

import datasets


REPO_URL = "https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/"
URLS = {"train": REPO_URL + "wikiann-bn-train.jsonl", "validation": REPO_URL + "wikiann-bn-validation.jsonl"}


class __DummyDataset1__(datasets.GeneratorBasedBuilder):

    def _info(self):
        features = datasets.Features(
            {
                "tokens": datasets.Sequence(datasets.Value("string")),
                "ner_tags": datasets.Sequence(
                    datasets.features.ClassLabel(
                        names=[
                            "O",
                            "B-PER",
                            "I-PER",
                            "B-ORG",
                            "I-ORG",
                            "B-LOC",
                            "I-LOC",
                        ]
                    )
                ),
                "langs": datasets.Sequence(datasets.Value("string")),
                "spans": datasets.Sequence(datasets.Value("string")),
            }
        )
        return datasets.DatasetInfo(features=features)

    def _split_generators(self, dl_manager):
        dl_path = dl_manager.download(URLS)
        return [
            datasets.SplitGenerator(datasets.Split.TRAIN, gen_kwargs={"filepath": dl_path["train"]}),
            datasets.SplitGenerator(datasets.Split.VALIDATION, gen_kwargs={"filepath": dl_path["validation"]}),
        ]

    def _generate_examples(self, filepath):
        with open(filepath, "r", encoding="utf-8") as f:
            for i, line in enumerate(f):
                yield i, json.loads(line)
'''






@pytest.fixture
def        __lowerCAmelCase		(	)	->    Any:
					return DATASET_LOADING_SCRIPT_NAME






@pytest.fixture
def        __lowerCAmelCase		(	)	->    Union[str, Any]:
					return DATASET_LOADING_SCRIPT_CODE






@pytest.fixture
def        __lowerCAmelCase		(	__lowerCamelCase						:     Optional[Any]					, __lowerCamelCase						:     Tuple					, __lowerCamelCase						:     str						)	->    List[str]:
					__lowerCAmelCase						 =dataset_loading_script_name
					__lowerCAmelCase						 =tmp_path / """datasets""" / script_name
					script_dir.mkdir(parents=__lowerCamelCase						)
					__lowerCAmelCase						 =script_dir / f"""{script_name}.py"""
					with open(__lowerCamelCase					, """w"""						) as f:
										f.write(__lowerCamelCase						)
					return str(__lowerCamelCase						)




