


'''simple docstring'''
import os
import textwrap

import pyarrow as pa
import pytest

from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv

from ..utils import require_pil





@pytest.fixture
def 					a__       (					lowerCAmelCase__						)    ->	Optional[Any]:
    UpperCAmelCase__			:		Dict													=				tmp_path / '''file.csv'''
    UpperCAmelCase__			:		Optional[int]													=				textwrap.dedent(
        '''\
        header1,header2
        1,2
        10,20
        '''						)
    with open(lowerCAmelCase__  ,					'''w'''						) as f:
        f.write(lowerCAmelCase__						)
    return str(lowerCAmelCase__						)





@pytest.fixture
def 					a__       (					lowerCAmelCase__						)    ->	Dict:
    UpperCAmelCase__			:		List[Any]													=				tmp_path / '''malformed_file.csv'''
    UpperCAmelCase__			:		Any													=				textwrap.dedent(
        '''\
        header1,header2
        1,2
        10,20,
        '''						)
    with open(lowerCAmelCase__  ,					'''w'''						) as f:
        f.write(lowerCAmelCase__						)
    return str(lowerCAmelCase__						)





@pytest.fixture
def 					a__       (					lowerCAmelCase__  ,					lowerCAmelCase__						)    ->	Dict:
    UpperCAmelCase__			:		Dict													=				tmp_path / '''csv_with_image.csv'''
    UpperCAmelCase__			:		int													=				textwrap.dedent(
        F"""\
        image
        {image_file}
        """						)
    with open(lowerCAmelCase__  ,					'''w'''						) as f:
        f.write(lowerCAmelCase__						)
    return str(lowerCAmelCase__						)





@pytest.fixture
def 					a__       (					lowerCAmelCase__						)    ->	Tuple:
    UpperCAmelCase__			:		List[Any]													=				tmp_path / '''csv_with_label.csv'''
    UpperCAmelCase__			:		List[str]													=				textwrap.dedent(
        '''\
        label
        good
        bad
        good
        '''						)
    with open(lowerCAmelCase__  ,					'''w'''						) as f:
        f.write(lowerCAmelCase__						)
    return str(lowerCAmelCase__						)





@pytest.fixture
def 					a__       (					lowerCAmelCase__						)    ->	Union[str, Any]:
    UpperCAmelCase__			:		Tuple													=				tmp_path / '''csv_with_int_list.csv'''
    UpperCAmelCase__			:		List[Any]													=				textwrap.dedent(
        '''\
        int_list
        1 2 3
        4 5 6
        7 8 9
        '''						)
    with open(lowerCAmelCase__  ,					'''w'''						) as f:
        f.write(lowerCAmelCase__						)
    return str(lowerCAmelCase__						)





def 					a__       (					lowerCAmelCase__  ,					lowerCAmelCase__  ,					lowerCAmelCase__						)    ->	Tuple:
    UpperCAmelCase__			:		Dict													=				Csv()
    UpperCAmelCase__			:		Dict													=				csv._generate_tables([[csv_file, malformed_csv_file]]						)
    with pytest.raises(lowerCAmelCase__  ,					match='''Error tokenizing data'''						):
        for _ in generator:
            pass
    assert any(
        record.levelname == '''ERROR'''
        and '''Failed to read file''' in record.message
        and os.path.basename(lowerCAmelCase__						) in record.message
        for record in caplog.records						)





@require_pil
def 					a__       (					lowerCAmelCase__						)    ->	Dict:
    with open(lowerCAmelCase__  ,					encoding='''utf-8'''						) as f:
        UpperCAmelCase__			:		int													=				f.read().splitlines()[1]
    UpperCAmelCase__			:		List[str]													=				Csv(encoding='''utf-8'''  ,					features=Features({'''image''': Image()}						)						)
    UpperCAmelCase__			:		str													=				csv._generate_tables([[csv_file_with_image]]						)
    UpperCAmelCase__			:		int													=				pa.concat_tables([table for _, table in generator]						)
    assert pa_table.schema.field('''image'''						).type == Image()()
    UpperCAmelCase__			:		List[Any]													=				pa_table.to_pydict()['''image''']
    assert generated_content == [{"path": image_file, "bytes": None}]





def 					a__       (					lowerCAmelCase__						)    ->	Any:
    with open(lowerCAmelCase__  ,					encoding='''utf-8'''						) as f:
        UpperCAmelCase__			:		int													=				f.read().splitlines()[1:]
    UpperCAmelCase__			:		Tuple													=				Csv(encoding='''utf-8'''  ,					features=Features({'''label''': ClassLabel(names=['''good''', '''bad''']						)}						)						)
    UpperCAmelCase__			:		Optional[Any]													=				csv._generate_tables([[csv_file_with_label]]						)
    UpperCAmelCase__			:		str													=				pa.concat_tables([table for _, table in generator]						)
    assert pa_table.schema.field('''label'''						).type == ClassLabel(names=['''good''', '''bad''']						)()
    UpperCAmelCase__			:		int													=				pa_table.to_pydict()['''label''']
    assert generated_content == [ClassLabel(names=['''good''', '''bad''']						).straint(lowerCAmelCase__						) for label in labels]







def 					a__       (					lowerCAmelCase__						)    ->	Dict:
    UpperCAmelCase__			:		List[Any]													=				Csv(encoding='''utf-8'''  ,					sep=''','''  ,					converters={'''int_list''': lambda lowerCAmelCase__						: [int(lowerCAmelCase__						) for i in x.split()]}						)
    UpperCAmelCase__			:		Tuple													=				csv._generate_tables([[csv_file_with_int_list]]						)
    UpperCAmelCase__			:		Tuple													=				pa.concat_tables([table for _, table in generator]						)
    assert pa.types.is_list(pa_table.schema.field('''int_list'''						).type						)
    UpperCAmelCase__			:		str													=				pa_table.to_pydict()['''int_list''']
    assert generated_content == [[1, 2, 3], [4, 5, 6], [7, 8, 9]]




'''simple docstring'''
import re

import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey

from ..utils import logging


UpperCamelCase__    					=      logging.get_logger(__name__)





def 					a__       (					lowerCAmelCase__						)    ->	Optional[Any]:
    UpperCAmelCase__			:		str													=				R'''\w+[.]\d+'''
    UpperCAmelCase__			:		List[Any]													=				re.findall(lowerCAmelCase__  ,					lowerCAmelCase__						)
    for pat in pats:
        UpperCAmelCase__			:		Union[str, Any]													=				key.replace(lowerCAmelCase__  ,					'''_'''.join(pat.split('''.'''						)						)						)
    return key





def 					a__       (					lowerCAmelCase__  ,					lowerCAmelCase__  ,					lowerCAmelCase__						)    ->	Union[str, Any]:
    UpperCAmelCase__			:		Optional[Any]													=				pt_tuple_key[:-1] + ('''scale''',)
    if (
        any('''norm''' in str_ for str_ in pt_tuple_key						)
        and (pt_tuple_key[-1] == "bias")
        and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
        and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
    ):
        UpperCAmelCase__			:		Union[str, Any]													=				pt_tuple_key[:-1] + ('''scale''',)
        return renamed_pt_tuple_key, pt_tensor
    elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
        UpperCAmelCase__			:		Optional[int]													=				pt_tuple_key[:-1] + ('''scale''',)
        return renamed_pt_tuple_key, pt_tensor

    # embedding
    if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
        UpperCAmelCase__			:		str													=				pt_tuple_key[:-1] + ('''embedding''',)
        return renamed_pt_tuple_key, pt_tensor

    # conv layer
    UpperCAmelCase__			:		Optional[Any]													=				pt_tuple_key[:-1] + ('''kernel''',)
    if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
        UpperCAmelCase__			:		List[Any]													=				pt_tensor.transpose(2  ,					3  ,					1  ,					0						)
        return renamed_pt_tuple_key, pt_tensor

    # linear layer
    UpperCAmelCase__			:		int													=				pt_tuple_key[:-1] + ('''kernel''',)
    if pt_tuple_key[-1] == "weight":
        UpperCAmelCase__			:		Optional[Any]													=				pt_tensor.T
        return renamed_pt_tuple_key, pt_tensor

    # old PyTorch layer norm weight
    UpperCAmelCase__			:		Union[str, Any]													=				pt_tuple_key[:-1] + ('''weight''',)
    if pt_tuple_key[-1] == "gamma":
        return renamed_pt_tuple_key, pt_tensor

    # old PyTorch layer norm bias
    UpperCAmelCase__			:		Optional[Any]													=				pt_tuple_key[:-1] + ('''bias''',)
    if pt_tuple_key[-1] == "beta":
        return renamed_pt_tuple_key, pt_tensor

    return pt_tuple_key, pt_tensor







def 					a__       (					lowerCAmelCase__  ,					lowerCAmelCase__  ,					lowerCAmelCase__=42						)    ->	Tuple:
    # Step 1: Convert pytorch tensor to numpy
    UpperCAmelCase__			:		int													=				{k: v.numpy() for k, v in pt_state_dict.items()}

    # Step 2: Since the model is stateless, get random Flax params
    UpperCAmelCase__			:		Tuple													=				flax_model.init_weights(PRNGKey(lowerCAmelCase__						)						)

    UpperCAmelCase__			:		Optional[Any]													=				flatten_dict(lowerCAmelCase__						)
    UpperCAmelCase__			:		List[str]													=				{}

    # Need to change some parameters name to match Flax names
    for pt_key, pt_tensor in pt_state_dict.items():
        UpperCAmelCase__			:		Optional[int]													=				rename_key(lowerCAmelCase__						)
        UpperCAmelCase__			:		str													=				tuple(renamed_pt_key.split('''.'''						)						)

        # Correctly rename weight parameters
        UpperCAmelCase__						,						UpperCAmelCase__			:		Optional[int]													=				rename_key_and_reshape_tensor(lowerCAmelCase__  ,					lowerCAmelCase__  ,					lowerCAmelCase__						)

        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}."""						)

        # also add unexpected weight so that warning is thrown
        UpperCAmelCase__			:		List[str]													=				jnp.asarray(lowerCAmelCase__						)

    return unflatten_dict(lowerCAmelCase__						)
