
"""simple docstring"""



import gc
import unittest

import torch
from parameterized import parameterized

from diffusers import AutoencoderKL
from diffusers.utils import floats_tensor, load_hf_numpy, require_torch_gpu, slow, torch_all_close, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()




class 		a_						( _UpperCAmelCase     ,						_UpperCAmelCase     ,						unittest.TestCase	):
    a      :				Any     =				AutoencoderKL
    a      :				Dict     =				'sample'
    a      :				List[str]     =				1e-2







    @property
    def      _snake_case					(			self			:       Any     )		->List[Any]:






        '''simple docstring'''






        _UpperCAmelCase          =   4
        _UpperCAmelCase          =   3
        _UpperCAmelCase          =   (32, 32)

        _UpperCAmelCase          =   floats_tensor((batch_size, num_channels) + sizes     ).to(__UpperCamelCase     )

        return {"sample": image}







    @property
    def      _snake_case					(			self			:       Tuple     )		->Optional[int]:






        '''simple docstring'''






        return (3, 32, 32)







    @property
    def      _snake_case					(			self			:       Tuple     )		->Any:






        '''simple docstring'''






        return (3, 32, 32)







    def      _snake_case					(			self			:       Dict     )		->Dict:






        '''simple docstring'''






        _UpperCAmelCase          =   {
            """block_out_channels""": [32, 64],
            """in_channels""": 3,
            """out_channels""": 3,
            """down_block_types""": ["""DownEncoderBlock2D""", """DownEncoderBlock2D"""],
            """up_block_types""": ["""UpDecoderBlock2D""", """UpDecoderBlock2D"""],
            """latent_channels""": 4,
        }
        _UpperCAmelCase          =   self.dummy_input
        return init_dict, inputs_dict







    def      _snake_case					(			self			:       int     )		->Any:






        '''simple docstring'''






        pass







    def      _snake_case					(			self			:       Union[str, Any]     )		->Optional[Any]:






        '''simple docstring'''






        pass







    @unittest.skipIf(torch_device == """mps"""     ,					"""Gradient checkpointing skipped on MPS"""     )
    def      _snake_case					(			self			:       Optional[int]     )		->List[Any]:






        '''simple docstring'''






        _UpperCAmelCase    ,_UpperCAmelCase          =   self.prepare_init_args_and_inputs_for_common()
        _UpperCAmelCase          =   self.model_class(**__UpperCamelCase     )
        model.to(__UpperCamelCase     )

        assert not model.is_gradient_checkpointing and model.training

        _UpperCAmelCase          =   model(**__UpperCamelCase     ).sample
        # run the backwards pass on the model. For backwards pass, for simplicity purpose,
        # we won't calculate the loss and rather backprop on out.sum()
        model.zero_grad()

        _UpperCAmelCase          =   torch.randn_like(__UpperCamelCase     )
        _UpperCAmelCase          =   (out - labels).mean()
        loss.backward()

        # re-instantiate the model now enabling gradient checkpointing
        _UpperCAmelCase          =   self.model_class(**__UpperCamelCase     )
        # clone model
        model_a.load_state_dict(model.state_dict()     )
        model_a.to(__UpperCamelCase     )
        model_a.enable_gradient_checkpointing()

        assert model_a.is_gradient_checkpointing and model_a.training

        _UpperCAmelCase          =   model_a(**__UpperCamelCase     ).sample
        # run the backwards pass on the model. For backwards pass, for simplicity purpose,
        # we won't calculate the loss and rather backprop on out.sum()
        model_a.zero_grad()
        _UpperCAmelCase          =   (out_a - labels).mean()
        loss_a.backward()

        # compare the output and parameters gradients
        self.assertTrue((loss - loss_a).abs() < 1e-5     )
        _UpperCAmelCase          =   dict(model.named_parameters()     )
        _UpperCAmelCase          =   dict(model_a.named_parameters()     )
        for name, param in named_params.items():
            self.assertTrue(torch_all_close(param.grad.data     ,					named_params_a[name].grad.data     ,					atol=5e-5     )     )







    def      _snake_case					(			self			:       int     )		->Optional[Any]:






        '''simple docstring'''






        _UpperCAmelCase    ,_UpperCAmelCase          =   AutoencoderKL.from_pretrained("""fusing/autoencoder-kl-dummy"""     ,					output_loading_info=__UpperCamelCase     )
        self.assertIsNotNone(__UpperCamelCase     )
        self.assertEqual(len(loading_info["""missing_keys"""]     )     ,					0     )

        model.to(__UpperCamelCase     )
        _UpperCAmelCase          =   model(**self.dummy_input     )

        assert image is not None, "Make sure output is not None"







    def      _snake_case					(			self			:       Union[str, Any]     )		->Dict:






        '''simple docstring'''






        _UpperCAmelCase          =   AutoencoderKL.from_pretrained("""fusing/autoencoder-kl-dummy"""     )
        _UpperCAmelCase          =   model.to(__UpperCamelCase     )
        model.eval()

        if torch_device == "mps":
            _UpperCAmelCase          =   torch.manual_seed(0     )
        else:
            _UpperCAmelCase          =   torch.Generator(device=__UpperCamelCase     ).manual_seed(0     )

        _UpperCAmelCase          =   torch.randn(
            1     ,					model.config.in_channels     ,					model.config.sample_size     ,					model.config.sample_size     ,					generator=torch.manual_seed(0     )     ,					)
        _UpperCAmelCase          =   image.to(__UpperCamelCase     )
        with torch.no_grad():
            _UpperCAmelCase          =   model(__UpperCamelCase     ,					sample_posterior=__UpperCamelCase     ,					generator=__UpperCamelCase     ).sample

        _UpperCAmelCase          =   output[0, -1, -3:, -3:].flatten().cpu()

        # Since the VAE Gaussian prior's generator is seeded on the appropriate device,
        # the expected output slices are not the same for CPU and GPU.
        if torch_device == "mps":
            _UpperCAmelCase          =   torch.tensor(
                [
                    -4.0078e-01,
                    -3.8323e-04,
                    -1.2681e-01,
                    -1.1462e-01,
                    2.0095e-01,
                    1.0893e-01,
                    -8.8247e-02,
                    -3.0361e-01,
                    -9.8644e-03,
                ]     )
        elif torch_device == "cpu":
            _UpperCAmelCase          =   torch.tensor(
                [-0.1_3_5_2, 0.0_8_7_8, 0.0_4_1_9, -0.0_8_1_8, -0.1_0_6_9, 0.0_6_8_8, -0.1_4_5_8, -0.4_4_4_6, -0.0_0_2_6]     )
        else:
            _UpperCAmelCase          =   torch.tensor(
                [-0.2_4_2_1, 0.4_6_4_2, 0.2_5_0_7, -0.0_4_3_8, 0.0_6_8_2, 0.3_1_6_0, -0.2_0_1_8, -0.0_7_2_7, 0.2_4_8_5]     )

        self.assertTrue(torch_all_close(__UpperCamelCase     ,					__UpperCamelCase     ,					rtol=1e-2     )     )







@slow
class 		a_						( unittest.TestCase	):







    def      _snake_case					(			self			:       int     ,					__UpperCamelCase			:       List[str]     ,					__UpperCamelCase			:       str     )		->str:






        '''simple docstring'''






        return f"""gaussian_noise_s={seed}_shape={"_".join([str(__UpperCamelCase     ) for s in shape]     )}.npy"""







    def      _snake_case					(			self			:       List[str]     )		->str:






        '''simple docstring'''






        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()







    def      _snake_case					(			self			:       Tuple     ,					__UpperCamelCase			:       Tuple=0     ,					__UpperCamelCase			:       List[Any]=(4, 3, 5_12, 5_12)     ,					__UpperCamelCase			:       Optional[Any]=False     )		->Union[str, Any]:






        '''simple docstring'''






        _UpperCAmelCase          =   torch.floataa if fpaa else torch.floataa
        _UpperCAmelCase          =   torch.from_numpy(load_hf_numpy(self.get_file_format(__UpperCamelCase     ,					__UpperCamelCase     )     )     ).to(__UpperCamelCase     ).to(__UpperCamelCase     )
        return image







    def      _snake_case					(			self			:       Dict     ,					__UpperCamelCase			:       Optional[int]="CompVis/stable-diffusion-v1-4"     ,					__UpperCamelCase			:       int=False     )		->Optional[Any]:






        '''simple docstring'''






        _UpperCAmelCase          =   """fp16""" if fpaa else None
        _UpperCAmelCase          =   torch.floataa if fpaa else torch.floataa

        _UpperCAmelCase          =   AutoencoderKL.from_pretrained(
            __UpperCamelCase     ,					subfolder="""vae"""     ,					torch_dtype=__UpperCamelCase     ,					revision=__UpperCamelCase     ,					)
        model.to(__UpperCamelCase     ).eval()

        return model







    def      _snake_case					(			self			:       Union[str, Any]     ,					__UpperCamelCase			:       Tuple=0     )		->Optional[int]:






        '''simple docstring'''






        if torch_device == "mps":
            return torch.manual_seed(__UpperCamelCase     )
        return torch.Generator(device=__UpperCamelCase     ).manual_seed(__UpperCamelCase     )







    @parameterized.expand(
        [
            # fmt: off
            [33, [-0.1_6_0_3, 0.9_8_7_8, -0.0_4_9_5, -0.0_7_9_0, -0.2_7_0_9, 0.8_3_7_5, -0.2_0_6_0, -0.0_8_2_4], [-0.2_3_9_5, 0.0_0_9_8, 0.0_1_0_2, -0.0_7_0_9, -0.2_8_4_0, -0.0_2_7_4, -0.0_7_1_8, -0.1_8_2_4]],
            [47, [-0.2_3_7_6, 0.1_1_6_8, 0.1_3_3_2, -0.4_8_4_0, -0.2_5_0_8, -0.0_7_9_1, -0.0_4_9_3, -0.4_0_8_9], [0.0_3_5_0, 0.0_8_4_7, 0.0_4_6_7, 0.0_3_4_4, -0.0_8_4_2, -0.0_5_4_7, -0.0_6_3_3, -0.1_1_3_1]],
            # fmt: on
        ]     )
    def      _snake_case					(			self			:       Union[str, Any]     ,					__UpperCamelCase			:       Dict     ,					__UpperCamelCase			:       Any     ,					__UpperCamelCase			:       Any     )		->Tuple:






        '''simple docstring'''






        _UpperCAmelCase          =   self.get_sd_vae_model()
        _UpperCAmelCase          =   self.get_sd_image(__UpperCamelCase     )
        _UpperCAmelCase          =   self.get_generator(__UpperCamelCase     )

        with torch.no_grad():
            _UpperCAmelCase          =   model(__UpperCamelCase     ,					generator=__UpperCamelCase     ,					sample_posterior=__UpperCamelCase     ).sample

        assert sample.shape == image.shape

        _UpperCAmelCase          =   sample[-1, -2:, -2:, :2].flatten().float().cpu()
        _UpperCAmelCase          =   torch.tensor(expected_slice_mps if torch_device == """mps""" else expected_slice     )

        assert torch_all_close(__UpperCamelCase     ,					__UpperCamelCase     ,					atol=3e-3     )







    @parameterized.expand(
        [
            # fmt: off
            [33, [-0.0_5_1_3, 0.0_2_8_9, 1.3_7_9_9, 0.2_1_6_6, -0.2_5_7_3, -0.0_8_7_1, 0.5_1_0_3, -0.0_9_9_9]],
            [47, [-0.4_1_2_8, -0.1_3_2_0, -0.3_7_0_4, 0.1_9_6_5, -0.4_1_1_6, -0.2_3_3_2, -0.3_3_4_0, 0.2_2_4_7]],
            # fmt: on
        ]     )
    @require_torch_gpu
    def      _snake_case					(			self			:       Union[str, Any]     ,					__UpperCamelCase			:       int     ,					__UpperCamelCase			:       str     )		->Any:






        '''simple docstring'''






        _UpperCAmelCase          =   self.get_sd_vae_model(fpaa=__UpperCamelCase     )
        _UpperCAmelCase          =   self.get_sd_image(__UpperCamelCase     ,					fpaa=__UpperCamelCase     )
        _UpperCAmelCase          =   self.get_generator(__UpperCamelCase     )

        with torch.no_grad():
            _UpperCAmelCase          =   model(__UpperCamelCase     ,					generator=__UpperCamelCase     ,					sample_posterior=__UpperCamelCase     ).sample

        assert sample.shape == image.shape

        _UpperCAmelCase          =   sample[-1, -2:, :2, -2:].flatten().float().cpu()
        _UpperCAmelCase          =   torch.tensor(__UpperCamelCase     )

        assert torch_all_close(__UpperCamelCase     ,					__UpperCamelCase     ,					atol=1e-2     )







    @parameterized.expand(
        [
            # fmt: off
            [33, [-0.1_6_0_9, 0.9_8_6_6, -0.0_4_8_7, -0.0_7_7_7, -0.2_7_1_6, 0.8_3_6_8, -0.2_0_5_5, -0.0_8_1_4], [-0.2_3_9_5, 0.0_0_9_8, 0.0_1_0_2, -0.0_7_0_9, -0.2_8_4_0, -0.0_2_7_4, -0.0_7_1_8, -0.1_8_2_4]],
            [47, [-0.2_3_7_7, 0.1_1_4_7, 0.1_3_3_3, -0.4_8_4_1, -0.2_5_0_6, -0.0_8_0_5, -0.0_4_9_1, -0.4_0_8_5], [0.0_3_5_0, 0.0_8_4_7, 0.0_4_6_7, 0.0_3_4_4, -0.0_8_4_2, -0.0_5_4_7, -0.0_6_3_3, -0.1_1_3_1]],
            # fmt: on
        ]     )
    def      _snake_case					(			self			:       int     ,					__UpperCamelCase			:       int     ,					__UpperCamelCase			:       Optional[Any]     ,					__UpperCamelCase			:       Any     )		->str:






        '''simple docstring'''






        _UpperCAmelCase          =   self.get_sd_vae_model()
        _UpperCAmelCase          =   self.get_sd_image(__UpperCamelCase     )

        with torch.no_grad():
            _UpperCAmelCase          =   model(__UpperCamelCase     ).sample

        assert sample.shape == image.shape

        _UpperCAmelCase          =   sample[-1, -2:, -2:, :2].flatten().float().cpu()
        _UpperCAmelCase          =   torch.tensor(expected_slice_mps if torch_device == """mps""" else expected_slice     )

        assert torch_all_close(__UpperCamelCase     ,					__UpperCamelCase     ,					atol=3e-3     )







    @parameterized.expand(
        [
            # fmt: off
            [13, [-0.2_0_5_1, -0.1_8_0_3, -0.2_3_1_1, -0.2_1_1_4, -0.3_2_9_2, -0.3_5_7_4, -0.2_9_5_3, -0.3_3_2_3]],
            [37, [-0.2_6_3_2, -0.2_6_2_5, -0.2_1_9_9, -0.2_7_4_1, -0.4_5_3_9, -0.4_9_9_0, -0.3_7_2_0, -0.4_9_2_5]],
            # fmt: on
        ]     )
    @require_torch_gpu
    def      _snake_case					(			self			:       Optional[int]     ,					__UpperCamelCase			:       List[str]     ,					__UpperCamelCase			:       List[str]     )		->Optional[int]:






        '''simple docstring'''






        _UpperCAmelCase          =   self.get_sd_vae_model()
        _UpperCAmelCase          =   self.get_sd_image(__UpperCamelCase     ,					shape=(3, 4, 64, 64)     )

        with torch.no_grad():
            _UpperCAmelCase          =   model.decode(__UpperCamelCase     ).sample

        assert list(sample.shape     ) == [3, 3, 5_12, 5_12]

        _UpperCAmelCase          =   sample[-1, -2:, :2, -2:].flatten().cpu()
        _UpperCAmelCase          =   torch.tensor(__UpperCamelCase     )

        assert torch_all_close(__UpperCamelCase     ,					__UpperCamelCase     ,					atol=1e-3     )







    @parameterized.expand(
        [
            # fmt: off
            [27, [-0.0_3_6_9, 0.0_2_0_7, -0.0_7_7_6, -0.0_6_8_2, -0.1_7_4_7, -0.1_9_3_0, -0.1_4_6_5, -0.2_0_3_9]],
            [16, [-0.1_6_2_8, -0.2_1_3_4, -0.2_7_4_7, -0.2_6_4_2, -0.3_7_7_4, -0.4_4_0_4, -0.3_6_8_7, -0.4_2_7_7]],
            # fmt: on
        ]     )
    @require_torch_gpu
    def      _snake_case					(			self			:       List[str]     ,					__UpperCamelCase			:       Optional[Any]     ,					__UpperCamelCase			:       Union[str, Any]     )		->List[Any]:






        '''simple docstring'''






        _UpperCAmelCase          =   self.get_sd_vae_model(fpaa=__UpperCamelCase     )
        _UpperCAmelCase          =   self.get_sd_image(__UpperCamelCase     ,					shape=(3, 4, 64, 64)     ,					fpaa=__UpperCamelCase     )

        with torch.no_grad():
            _UpperCAmelCase          =   model.decode(__UpperCamelCase     ).sample

        assert list(sample.shape     ) == [3, 3, 5_12, 5_12]

        _UpperCAmelCase          =   sample[-1, -2:, :2, -2:].flatten().float().cpu()
        _UpperCAmelCase          =   torch.tensor(__UpperCamelCase     )

        assert torch_all_close(__UpperCamelCase     ,					__UpperCamelCase     ,					atol=5e-3     )







    @parameterized.expand([(13,), (16,), (27,)]     )
    @require_torch_gpu
    @unittest.skipIf(not is_xformers_available()     ,					reason="""xformers is not required when using PyTorch 2.0."""     )
    def      _snake_case					(			self			:       Optional[Any]     ,					__UpperCamelCase			:       List[str]     )		->Any:






        '''simple docstring'''






        _UpperCAmelCase          =   self.get_sd_vae_model(fpaa=__UpperCamelCase     )
        _UpperCAmelCase          =   self.get_sd_image(__UpperCamelCase     ,					shape=(3, 4, 64, 64)     ,					fpaa=__UpperCamelCase     )

        with torch.no_grad():
            _UpperCAmelCase          =   model.decode(__UpperCamelCase     ).sample

        model.enable_xformers_memory_efficient_attention()
        with torch.no_grad():
            _UpperCAmelCase          =   model.decode(__UpperCamelCase     ).sample

        assert list(sample.shape     ) == [3, 3, 5_12, 5_12]

        assert torch_all_close(__UpperCamelCase     ,					__UpperCamelCase     ,					atol=1e-1     )







    @parameterized.expand([(13,), (16,), (37,)]     )
    @require_torch_gpu
    @unittest.skipIf(not is_xformers_available()     ,					reason="""xformers is not required when using PyTorch 2.0."""     )
    def      _snake_case					(			self			:       Union[str, Any]     ,					__UpperCamelCase			:       Dict     )		->Tuple:






        '''simple docstring'''






        _UpperCAmelCase          =   self.get_sd_vae_model()
        _UpperCAmelCase          =   self.get_sd_image(__UpperCamelCase     ,					shape=(3, 4, 64, 64)     )

        with torch.no_grad():
            _UpperCAmelCase          =   model.decode(__UpperCamelCase     ).sample

        model.enable_xformers_memory_efficient_attention()
        with torch.no_grad():
            _UpperCAmelCase          =   model.decode(__UpperCamelCase     ).sample

        assert list(sample.shape     ) == [3, 3, 5_12, 5_12]

        assert torch_all_close(__UpperCamelCase     ,					__UpperCamelCase     ,					atol=1e-2     )







    @parameterized.expand(
        [
            # fmt: off
            [33, [-0.3_0_0_1, 0.0_9_1_8, -2.6_9_8_4, -3.9_7_2_0, -3.2_0_9_9, -5.0_3_5_3, 1.7_3_3_8, -0.2_0_6_5, 3.4_2_6_7]],
            [47, [-1.5_0_3_0, -4.3_8_7_1, -6.0_3_5_5, -9.1_1_5_7, -1.6_6_6_1, -2.7_8_5_3, 2.1_6_0_7, -5.0_8_2_3, 2.5_6_3_3]],
            # fmt: on
        ]     )
    def      _snake_case					(			self			:       str     ,					__UpperCamelCase			:       Tuple     ,					__UpperCamelCase			:       Optional[Any]     )		->Optional[int]:






        '''simple docstring'''






        _UpperCAmelCase          =   self.get_sd_vae_model()
        _UpperCAmelCase          =   self.get_sd_image(__UpperCamelCase     )
        _UpperCAmelCase          =   self.get_generator(__UpperCamelCase     )

        with torch.no_grad():
            _UpperCAmelCase          =   model.encode(__UpperCamelCase     ).latent_dist
            _UpperCAmelCase          =   dist.sample(generator=__UpperCamelCase     )

        assert list(sample.shape     ) == [image.shape[0], 4] + [i // 8 for i in image.shape[2:]]

        _UpperCAmelCase          =   sample[0, -1, -3:, -3:].flatten().cpu()
        _UpperCAmelCase          =   torch.tensor(__UpperCamelCase     )

        _UpperCAmelCase          =   3e-3 if torch_device != """mps""" else 1e-2
        assert torch_all_close(__UpperCamelCase     ,					__UpperCamelCase     ,					atol=__UpperCamelCase     )

"""simple docstring"""



import copy
import os
import tempfile
from unittest import TestCase
from unittest.mock import patch

import numpy as np
import pyarrow as pa
import pyarrow.parquet as pq
import pytest

from datasets.arrow_writer import ArrowWriter, OptimizedTypedSequence, ParquetWriter, TypedSequence
from datasets.features import ArrayaD, ClassLabel, Features, Image, Value
from datasets.features.features import ArrayaDExtensionType, cast_to_python_objects
from datasets.keyhash import DuplicatedKeysError, InvalidKeyError

from .utils import require_pil




class 		a_						( _UpperCAmelCase	):







    def      _snake_case					(			self			:       str     )		->str:






        '''simple docstring'''






        _UpperCAmelCase          =   pa.array(TypedSequence([1, 2, 3]     )     )
        self.assertEqual(arr.type     ,					pa.intaa()     )







    def      _snake_case					(			self			:       Any     )		->List[str]:






        '''simple docstring'''






        with self.assertRaises(__UpperCamelCase     ):
            _UpperCAmelCase          =   pa.array(TypedSequence([1, 2, 3]     )     ,					type=pa.intaa()     )







    def      _snake_case					(			self			:       Optional[int]     )		->Tuple:






        '''simple docstring'''






        with self.assertRaises(__UpperCamelCase     ):
            _UpperCAmelCase          =   pa.array(TypedSequence([1, 2, 3]     ,					try_type=Value("""bool"""     )     ,					type=Value("""int64"""     )     )     )







    def      _snake_case					(			self			:       List[Any]     )		->Dict:






        '''simple docstring'''






        _UpperCAmelCase          =   pa.array(TypedSequence([1, 2, 3]     ,					type=Value("""int32"""     )     )     )
        self.assertEqual(arr.type     ,					pa.intaa()     )







    def      _snake_case					(			self			:       str     )		->Dict:






        '''simple docstring'''






        with self.assertRaises((TypeError, pa.lib.ArrowInvalid)     ):
            _UpperCAmelCase          =   pa.array(TypedSequence(["""foo""", """bar"""]     ,					type=Value("""int64"""     )     )     )







    def      _snake_case					(			self			:       Union[str, Any]     )		->str:






        '''simple docstring'''






        _UpperCAmelCase          =   pa.array(TypedSequence([1, 2, 3]     ,					try_type=Value("""int32"""     )     )     )
        self.assertEqual(arr.type     ,					pa.intaa()     )







    def      _snake_case					(			self			:       List[str]     )		->Any:






        '''simple docstring'''






        _UpperCAmelCase          =   pa.array(TypedSequence(["""foo""", """bar"""]     ,					try_type=Value("""int64"""     )     )     )
        self.assertEqual(arr.type     ,					pa.string()     )







    def      _snake_case					(			self			:       List[Any]     )		->List[Any]:






        '''simple docstring'''






        _UpperCAmelCase          =   pa.array(TypedSequence([[[1, 2, 3]]]     ,					type=ArrayaD((1, 3)     ,					"""int64"""     )     )     )
        self.assertEqual(arr.type     ,					ArrayaDExtensionType((1, 3)     ,					"""int64"""     )     )







    def      _snake_case					(			self			:       List[Any]     )		->Optional[Any]:






        '''simple docstring'''






        with self.assertRaises((TypeError, pa.lib.ArrowInvalid)     ):
            _UpperCAmelCase          =   pa.array(TypedSequence(["""foo""", """bar"""]     ,					type=ArrayaD((1, 3)     ,					"""int64"""     )     )     )







    def      _snake_case					(			self			:       List[str]     )		->int:






        '''simple docstring'''






        _UpperCAmelCase          =   pa.array(TypedSequence([[[1, 2, 3]]]     ,					try_type=ArrayaD((1, 3)     ,					"""int64"""     )     )     )
        self.assertEqual(arr.type     ,					ArrayaDExtensionType((1, 3)     ,					"""int64"""     )     )







    def      _snake_case					(			self			:       Optional[int]     )		->Dict:






        '''simple docstring'''






        _UpperCAmelCase          =   pa.array(TypedSequence(["""foo""", """bar"""]     ,					try_type=ArrayaD((1, 3)     ,					"""int64"""     )     )     )
        self.assertEqual(arr.type     ,					pa.string()     )







    @require_pil
    def      _snake_case					(			self			:       str     )		->Optional[Any]:






        '''simple docstring'''






        import PIL.Image

        _UpperCAmelCase          =   PIL.Image.fromarray(np.arange(10     ,					dtype=np.uinta     ).reshape(2     ,					5     )     )
        with patch(
            """datasets.arrow_writer.cast_to_python_objects"""     ,					side_effect=__UpperCamelCase     ) as mock_cast_to_python_objects:
            _UpperCAmelCase          =   pa.array(TypedSequence([{"""path""": None, """bytes""": b"""image_bytes"""}, pil_image]     ,					type=Image()     )     )
            _UpperCAmelCase    ,_UpperCAmelCase          =   mock_cast_to_python_objects.call_args_list[-1]
            self.assertIn("""optimize_list_casting"""     ,					__UpperCamelCase     )
            self.assertFalse(kwargs["""optimize_list_casting"""]     )







def  _UpperCamelCase	(						_A					,      _A      )    ->  Any:





    """simple docstring"""

    _UpperCAmelCase          =   pa.BufferReader(_A      ) if isinstance(_A					,      pa.Buffer      ) else pa.memory_map(_A      )
    _UpperCAmelCase          =   pa.ipc.open_stream(_A      )
    _UpperCAmelCase          =   f.read_all()
    assert len(pa_table.to_batches()      ) == expected_num_chunks
    assert pa_table.to_pydict() == {"col_1": ["foo", "bar"], "col_2": [1, 2]}
    del pa_table


@pytest.mark.parametrize("""writer_batch_size"""					,      [None, 1, 1_0]      )
@pytest.mark.parametrize(
    """fields"""					,      [None, {"""col_1""": pa.string(), """col_2""": pa.intaa()}, {"""col_1""": pa.string(), """col_2""": pa.intaa()}]      )
def  _UpperCamelCase	(						_A					,      _A      )    ->  Optional[Any]:





    """simple docstring"""

    _UpperCAmelCase          =   pa.BufferOutputStream()
    _UpperCAmelCase          =   pa.schema(_A      ) if fields else None
    with ArrowWriter(stream=_A					,      schema=_A					,      writer_batch_size=_A      ) as writer:
        writer.write({"""col_1""": """foo""", """col_2""": 1}      )
        writer.write({"""col_1""": """bar""", """col_2""": 2}      )
        _UpperCAmelCase    ,_UpperCAmelCase          =   writer.finalize()
    assert num_examples == 2
    assert num_bytes > 0
    if not fields:
        _UpperCAmelCase          =   {"""col_1""": pa.string(), """col_2""": pa.intaa()}
    assert writer._schema == pa.schema(_A					,      metadata=writer._schema.metadata      )
    _check_output(output.getvalue()					,      expected_num_chunks=num_examples if writer_batch_size == 1 else 1      )


def  _UpperCamelCase	(						)    ->  Union[str, Any]:





    """simple docstring"""

    _UpperCAmelCase          =   pa.BufferOutputStream()
    _UpperCAmelCase          =   Features({"""labels""": ClassLabel(names=["""neg""", """pos"""]      )}      )
    with ArrowWriter(stream=_A					,      features=_A      ) as writer:
        writer.write({"""labels""": 0}      )
        writer.write({"""labels""": 1}      )
        _UpperCAmelCase    ,_UpperCAmelCase          =   writer.finalize()
    assert num_examples == 2
    assert num_bytes > 0
    assert writer._schema == features.arrow_schema
    assert writer._schema.metadata == features.arrow_schema.metadata
    _UpperCAmelCase          =   pa.BufferReader(output.getvalue()      )
    _UpperCAmelCase          =   pa.ipc.open_stream(_A      )
    _UpperCAmelCase          =   f.read_all()
    _UpperCAmelCase          =   pa_table.schema
    assert pa_table.num_rows == 2
    assert schema == features.arrow_schema
    assert schema.metadata == features.arrow_schema.metadata
    assert features == Features.from_arrow_schema(_A      )


@pytest.mark.parametrize("""writer_batch_size"""					,      [None, 1, 1_0]      )
def  _UpperCamelCase	(						_A      )    ->  Optional[int]:





    """simple docstring"""

    _UpperCAmelCase          =   pa.BufferOutputStream()
    with ArrowWriter(
        stream=_A					,      writer_batch_size=_A					,      hash_salt="""split_name"""					,      check_duplicates=_A					,      ) as writer:
        with pytest.raises(_A      ):
            writer.write({"""col_1""": """foo""", """col_2""": 1}					,      key=[1, 2]      )
            _UpperCAmelCase    ,_UpperCAmelCase          =   writer.finalize()


@pytest.mark.parametrize("""writer_batch_size"""					,      [None, 2, 1_0]      )
def  _UpperCamelCase	(						_A      )    ->  List[Any]:





    """simple docstring"""

    _UpperCAmelCase          =   pa.BufferOutputStream()
    with ArrowWriter(
        stream=_A					,      writer_batch_size=_A					,      hash_salt="""split_name"""					,      check_duplicates=_A					,      ) as writer:
        with pytest.raises(_A      ):
            writer.write({"""col_1""": """foo""", """col_2""": 1}					,      key=1_0      )
            writer.write({"""col_1""": """bar""", """col_2""": 2}					,      key=1_0      )
            _UpperCAmelCase    ,_UpperCAmelCase          =   writer.finalize()


@pytest.mark.parametrize("""writer_batch_size"""					,      [None, 2, 1_0]      )
def  _UpperCamelCase	(						_A      )    ->  Tuple:





    """simple docstring"""

    _UpperCAmelCase          =   pa.BufferOutputStream()
    with ArrowWriter(
        stream=_A					,      writer_batch_size=_A					,      hash_salt="""split_name"""					,      check_duplicates=_A					,      ) as writer:
        writer.write({"""col_1""": """foo""", """col_2""": 1}					,      key=1      )
        writer.write({"""col_1""": """bar""", """col_2""": 2}					,      key=2      )
        _UpperCAmelCase    ,_UpperCAmelCase          =   writer.finalize()
    assert num_examples == 2
    assert num_bytes > 0
    _check_output(output.getvalue()					,      expected_num_chunks=num_examples if writer_batch_size == 1 else 1      )


@pytest.mark.parametrize("""writer_batch_size"""					,      [None, 1, 1_0]      )
@pytest.mark.parametrize(
    """fields"""					,      [None, {"""col_1""": pa.string(), """col_2""": pa.intaa()}, {"""col_1""": pa.string(), """col_2""": pa.intaa()}]      )
def  _UpperCamelCase	(						_A					,      _A      )    ->  List[Any]:





    """simple docstring"""

    _UpperCAmelCase          =   pa.BufferOutputStream()
    _UpperCAmelCase          =   pa.schema(_A      ) if fields else None
    with ArrowWriter(stream=_A					,      schema=_A					,      writer_batch_size=_A      ) as writer:
        writer.write_batch({"""col_1""": ["""foo""", """bar"""], """col_2""": [1, 2]}      )
        writer.write_batch({"""col_1""": [], """col_2""": []}      )
        _UpperCAmelCase    ,_UpperCAmelCase          =   writer.finalize()
    assert num_examples == 2
    assert num_bytes > 0
    if not fields:
        _UpperCAmelCase          =   {"""col_1""": pa.string(), """col_2""": pa.intaa()}
    assert writer._schema == pa.schema(_A					,      metadata=writer._schema.metadata      )
    _check_output(output.getvalue()					,      expected_num_chunks=num_examples if writer_batch_size == 1 else 1      )


@pytest.mark.parametrize("""writer_batch_size"""					,      [None, 1, 1_0]      )
@pytest.mark.parametrize(
    """fields"""					,      [None, {"""col_1""": pa.string(), """col_2""": pa.intaa()}, {"""col_1""": pa.string(), """col_2""": pa.intaa()}]      )
def  _UpperCamelCase	(						_A					,      _A      )    ->  Any:





    """simple docstring"""

    _UpperCAmelCase          =   pa.BufferOutputStream()
    _UpperCAmelCase          =   pa.schema(_A      ) if fields else None
    with ArrowWriter(stream=_A					,      schema=_A					,      writer_batch_size=_A      ) as writer:
        writer.write_table(pa.Table.from_pydict({"""col_1""": ["""foo""", """bar"""], """col_2""": [1, 2]}      )      )
        _UpperCAmelCase    ,_UpperCAmelCase          =   writer.finalize()
    assert num_examples == 2
    assert num_bytes > 0
    if not fields:
        _UpperCAmelCase          =   {"""col_1""": pa.string(), """col_2""": pa.intaa()}
    assert writer._schema == pa.schema(_A					,      metadata=writer._schema.metadata      )
    _check_output(output.getvalue()					,      expected_num_chunks=num_examples if writer_batch_size == 1 else 1      )


@pytest.mark.parametrize("""writer_batch_size"""					,      [None, 1, 1_0]      )
@pytest.mark.parametrize(
    """fields"""					,      [None, {"""col_1""": pa.string(), """col_2""": pa.intaa()}, {"""col_1""": pa.string(), """col_2""": pa.intaa()}]      )
def  _UpperCamelCase	(						_A					,      _A      )    ->  List[Any]:





    """simple docstring"""

    _UpperCAmelCase          =   pa.BufferOutputStream()
    _UpperCAmelCase          =   pa.schema(_A      ) if fields else None
    with ArrowWriter(stream=_A					,      schema=_A					,      writer_batch_size=_A      ) as writer:
        writer.write_row(pa.Table.from_pydict({"""col_1""": ["""foo"""], """col_2""": [1]}      )      )
        writer.write_row(pa.Table.from_pydict({"""col_1""": ["""bar"""], """col_2""": [2]}      )      )
        _UpperCAmelCase    ,_UpperCAmelCase          =   writer.finalize()
    assert num_examples == 2
    assert num_bytes > 0
    if not fields:
        _UpperCAmelCase          =   {"""col_1""": pa.string(), """col_2""": pa.intaa()}
    assert writer._schema == pa.schema(_A					,      metadata=writer._schema.metadata      )
    _check_output(output.getvalue()					,      expected_num_chunks=num_examples if writer_batch_size == 1 else 1      )


def  _UpperCamelCase	(						)    ->  str:





    """simple docstring"""

    with tempfile.TemporaryDirectory() as tmp_dir:
        _UpperCAmelCase          =   {"""col_1""": pa.string(), """col_2""": pa.intaa()}
        _UpperCAmelCase          =   os.path.join(_A					,      """test.arrow"""      )
        with ArrowWriter(path=_A					,      schema=pa.schema(_A      )      ) as writer:
            writer.write_batch({"""col_1""": ["""foo""", """bar"""], """col_2""": [1, 2]}      )
            _UpperCAmelCase    ,_UpperCAmelCase          =   writer.finalize()
        assert num_examples == 2
        assert num_bytes > 0
        assert writer._schema == pa.schema(_A					,      metadata=writer._schema.metadata      )
        _check_output(_A					,      1      )


def  _UpperCamelCase	(						_A      )    ->  int:





    """simple docstring"""

    if pa.types.is_list(_A      ):
        return get_base_dtype(arr_type.value_type      )
    else:
        return arr_type


def  _UpperCamelCase	(						_A					,      _A      )    ->  Any:





    """simple docstring"""

    if isinstance(lst[0]					,      _A      ):
        change_first_primitive_element_in_list(lst[0]					,      _A      )
    else:
        _UpperCAmelCase          =   value


@pytest.mark.parametrize("""optimized_int_type, expected_dtype"""					,      [(None, pa.intaa()), (Value("""int32"""      ), pa.intaa())]      )
@pytest.mark.parametrize("""sequence"""					,      [[1, 2, 3], [[1, 2, 3]], [[[1, 2, 3]]]]      )
def  _UpperCamelCase	(						_A					,      _A					,      _A      )    ->  Dict:





    """simple docstring"""

    _UpperCAmelCase          =   pa.array(TypedSequence(_A					,      optimized_int_type=_A      )      )
    assert get_base_dtype(arr.type      ) == expected_dtype


@pytest.mark.parametrize(
    """col, expected_dtype"""					,      [
        ("""attention_mask""", pa.inta()),
        ("""special_tokens_mask""", pa.inta()),
        ("""token_type_ids""", pa.inta()),
        ("""input_ids""", pa.intaa()),
        ("""other""", pa.intaa()),
    ]					,      )
@pytest.mark.parametrize("""sequence"""					,      [[1, 2, 3], [[1, 2, 3]], [[[1, 2, 3]]]]      )
def  _UpperCamelCase	(						_A					,      _A					,      _A      )    ->  str:





    """simple docstring"""

    _UpperCAmelCase          =   pa.array(OptimizedTypedSequence(_A					,      col=_A      )      )
    assert get_base_dtype(arr.type      ) == expected_dtype

    # not in range
    if col != "other":
        # avoids errors due to in-place modifications
        _UpperCAmelCase          =   copy.deepcopy(_A      )
        _UpperCAmelCase          =   np.iinfo(expected_dtype.to_pandas_dtype()      ).max + 1
        change_first_primitive_element_in_list(_A					,      _A      )
        _UpperCAmelCase          =   pa.array(OptimizedTypedSequence(_A					,      col=_A      )      )
        assert get_base_dtype(arr.type      ) == pa.intaa()


@pytest.mark.parametrize("""raise_exception"""					,      [False, True]      )
def  _UpperCamelCase	(						_A					,      _A      )    ->  Dict:





    """simple docstring"""

    _UpperCAmelCase          =   str(tmp_path / """dataset-train.arrow"""      )
    try:
        with ArrowWriter(path=_A      ) as writer:
            if raise_exception:
                raise pa.lib.ArrowInvalid()
            else:
                writer.stream.close()
    except pa.lib.ArrowInvalid:
        pass
    finally:
        assert writer.stream.closed


def  _UpperCamelCase	(						_A      )    ->  Dict:





    """simple docstring"""

    _UpperCAmelCase          =   """mock://dataset-train.arrow"""
    with ArrowWriter(path=_A					,      storage_options=mockfs.storage_options      ) as writer:
        assert isinstance(writer._fs					,      type(_A      )      )
        assert writer._fs.storage_options == mockfs.storage_options
        writer.write({"""col_1""": """foo""", """col_2""": 1}      )
        writer.write({"""col_1""": """bar""", """col_2""": 2}      )
        _UpperCAmelCase    ,_UpperCAmelCase          =   writer.finalize()
    assert num_examples == 2
    assert num_bytes > 0
    assert mockfs.exists(_A      )


def  _UpperCamelCase	(						)    ->  Dict:





    """simple docstring"""

    _UpperCAmelCase          =   pa.BufferOutputStream()
    with ParquetWriter(stream=_A      ) as writer:
        writer.write({"""col_1""": """foo""", """col_2""": 1}      )
        writer.write({"""col_1""": """bar""", """col_2""": 2}      )
        _UpperCAmelCase    ,_UpperCAmelCase          =   writer.finalize()
    assert num_examples == 2
    assert num_bytes > 0
    _UpperCAmelCase          =   pa.BufferReader(output.getvalue()      )
    _UpperCAmelCase          =   pq.read_table(_A      )
    assert pa_table.to_pydict() == {"col_1": ["foo", "bar"], "col_2": [1, 2]}


@require_pil
@pytest.mark.parametrize("""embed_local_files"""					,      [False, True]      )
def  _UpperCamelCase	(						_A					,      _A      )    ->  Any:





    """simple docstring"""

    import PIL.Image

    _UpperCAmelCase          =   str(tmp_path / """test_image_rgb.jpg"""      )
    PIL.Image.fromarray(np.zeros((5, 5)					,      dtype=np.uinta      )      ).save(_A					,      format="""png"""      )
    _UpperCAmelCase          =   pa.BufferOutputStream()
    with ParquetWriter(
        stream=_A					,      features=Features({"""image""": Image()}      )					,      embed_local_files=_A      ) as writer:
        writer.write({"""image""": image_path}      )
        writer.finalize()
    _UpperCAmelCase          =   pa.BufferReader(output.getvalue()      )
    _UpperCAmelCase          =   pq.read_table(_A      )
    _UpperCAmelCase          =   pa_table.to_pydict()
    if embed_local_files:
        assert isinstance(out["""image"""][0]["""path"""]					,      _A      )
        with open(_A					,      """rb"""      ) as f:
            assert out["image"][0]["bytes"] == f.read()
    else:
        assert out["image"][0]["path"] == image_path
        assert out["image"][0]["bytes"] is None







def  _UpperCamelCase	(						)    ->  int:





    """simple docstring"""

    _UpperCAmelCase          =   pa.schema([pa.field("""col_1"""					,      pa.string()					,      nullable=_A      )]      )
    _UpperCAmelCase          =   pa.BufferOutputStream()
    with ArrowWriter(stream=_A      ) as writer:
        writer._build_writer(inferred_schema=_A      )
        assert writer._schema == pa.schema([pa.field("""col_1"""					,      pa.string()      )]      )