
"""simple docstring"""



from __future__ import annotations

import unittest

from transformers import MobileBertConfig, is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
 import tensorflow as tf

 from transformers import (
     TF_MODEL_FOR_PRETRAINING_MAPPING,
     TFMobileBertForMaskedLM,
     TFMobileBertForMultipleChoice,
     TFMobileBertForNextSentencePrediction,
     TFMobileBertForPreTraining,
     TFMobileBertForQuestionAnswering,
     TFMobileBertForSequenceClassification,
     TFMobileBertForTokenClassification,
     TFMobileBertModel,
 )




@require_tf
class 		a_						( _UpperCAmelCase     ,						_UpperCAmelCase     ,						unittest.TestCase	):
    a      :				int     =				(
        (
            TFMobileBertModel,
            TFMobileBertForMaskedLM,
            TFMobileBertForNextSentencePrediction,
            TFMobileBertForPreTraining,
            TFMobileBertForQuestionAnswering,
            TFMobileBertForSequenceClassification,
            TFMobileBertForTokenClassification,
            TFMobileBertForMultipleChoice,
        )
        if is_tf_available()
        else ()
    )
    a      :				Tuple     =				(
        {
            'feature-extraction': TFMobileBertModel,
            'fill-mask': TFMobileBertForMaskedLM,
            'question-answering': TFMobileBertForQuestionAnswering,
            'text-classification': TFMobileBertForSequenceClassification,
            'token-classification': TFMobileBertForTokenClassification,
            'zero-shot': TFMobileBertForSequenceClassification,
        }
        if is_tf_available()
        else {}
    )
    a      :				Tuple     =				False
    a      :				List[Any]     =				False







    def      _snake_case					(			self			:       Tuple     ,					__UpperCamelCase			:       Tuple     ,					__UpperCamelCase			:       Tuple     ,					__UpperCamelCase			:       Optional[Any]=False     )		->Optional[Any]:






        '''simple docstring'''






        _UpperCAmelCase          =   super()._prepare_for_class(__UpperCamelCase     ,					__UpperCamelCase     ,					return_labels=__UpperCamelCase     )

        if return_labels:
            if model_class in get_values(__UpperCamelCase     ):
                _UpperCAmelCase          =   tf.zeros(self.model_tester.batch_size     ,					dtype=tf.intaa     )

        return inputs_dict




    class 		a_						( _UpperCAmelCase	):







        def __init__(			self			:       Dict     ,					__UpperCamelCase			:       int     ,					__UpperCamelCase			:       Optional[Any]=13     ,					__UpperCamelCase			:       Optional[Any]=7     ,					__UpperCamelCase			:       int=True     ,					__UpperCamelCase			:       Tuple=True     ,					__UpperCamelCase			:       List[str]=True     ,					__UpperCamelCase			:       Any=True     ,					__UpperCamelCase			:       Union[str, Any]=99     ,					__UpperCamelCase			:       Dict=32     ,					__UpperCamelCase			:       List[str]=32     ,					__UpperCamelCase			:       Union[str, Any]=2     ,					__UpperCamelCase			:       Optional[Any]=4     ,					__UpperCamelCase			:       List[Any]=37     ,					__UpperCamelCase			:       Tuple="gelu"     ,					__UpperCamelCase			:       List[Any]=0.1     ,					__UpperCamelCase			:       List[Any]=0.1     ,					__UpperCamelCase			:       Dict=5_12     ,					__UpperCamelCase			:       Any=16     ,					__UpperCamelCase			:       Union[str, Any]=2     ,					__UpperCamelCase			:       str=0.0_2     ,					__UpperCamelCase			:       int=3     ,					__UpperCamelCase			:       List[Any]=4     ,					__UpperCamelCase			:       List[Any]=None     ,					)		->Any:






            '''simple docstring'''






            _UpperCAmelCase          =   parent
            _UpperCAmelCase          =   batch_size
            _UpperCAmelCase          =   seq_length
            _UpperCAmelCase          =   is_training
            _UpperCAmelCase          =   use_input_mask
            _UpperCAmelCase          =   use_token_type_ids
            _UpperCAmelCase          =   use_labels
            _UpperCAmelCase          =   vocab_size
            _UpperCAmelCase          =   hidden_size
            _UpperCAmelCase          =   num_hidden_layers
            _UpperCAmelCase          =   num_attention_heads
            _UpperCAmelCase          =   intermediate_size
            _UpperCAmelCase          =   hidden_act
            _UpperCAmelCase          =   hidden_dropout_prob
            _UpperCAmelCase          =   attention_probs_dropout_prob
            _UpperCAmelCase          =   max_position_embeddings
            _UpperCAmelCase          =   type_vocab_size
            _UpperCAmelCase          =   type_sequence_label_size
            _UpperCAmelCase          =   initializer_range
            _UpperCAmelCase          =   num_labels
            _UpperCAmelCase          =   num_choices
            _UpperCAmelCase          =   scope
            _UpperCAmelCase          =   embedding_size







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






            '''simple docstring'''






            _UpperCAmelCase          =   ids_tensor([self.batch_size, self.seq_length]     ,					self.vocab_size     )

            _UpperCAmelCase          =   None
            if self.use_input_mask:
                _UpperCAmelCase          =   random_attention_mask([self.batch_size, self.seq_length]     )

            _UpperCAmelCase          =   None
            if self.use_token_type_ids:
                _UpperCAmelCase          =   ids_tensor([self.batch_size, self.seq_length]     ,					self.type_vocab_size     )

            _UpperCAmelCase          =   None
            _UpperCAmelCase          =   None
            _UpperCAmelCase          =   None
            if self.use_labels:
                _UpperCAmelCase          =   ids_tensor([self.batch_size]     ,					self.type_sequence_label_size     )
                _UpperCAmelCase          =   ids_tensor([self.batch_size, self.seq_length]     ,					self.num_labels     )
                _UpperCAmelCase          =   ids_tensor([self.batch_size]     ,					self.num_choices     )

            _UpperCAmelCase          =   MobileBertConfig(
                vocab_size=self.vocab_size     ,					hidden_size=self.hidden_size     ,					num_hidden_layers=self.num_hidden_layers     ,					num_attention_heads=self.num_attention_heads     ,					intermediate_size=self.intermediate_size     ,					hidden_act=self.hidden_act     ,					hidden_dropout_prob=self.hidden_dropout_prob     ,					attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,					max_position_embeddings=self.max_position_embeddings     ,					type_vocab_size=self.type_vocab_size     ,					initializer_range=self.initializer_range     ,					embedding_size=self.embedding_size     ,					)

            return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels







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






            '''simple docstring'''






            _UpperCAmelCase          =   TFMobileBertModel(config=__UpperCamelCase     )
            _UpperCAmelCase          =   {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
            _UpperCAmelCase          =   model(__UpperCamelCase     )

            _UpperCAmelCase          =   [input_ids, input_mask]
            _UpperCAmelCase          =   model(__UpperCamelCase     )

            _UpperCAmelCase          =   model(__UpperCamelCase     )

            self.parent.assertEqual(
                result.last_hidden_state.shape     ,					(self.batch_size, self.seq_length, self.hidden_size)     )
            self.parent.assertEqual(result.pooler_output.shape     ,					(self.batch_size, self.hidden_size)     )







        def      _snake_case					(			self			:       Any     ,					__UpperCamelCase			:       str     ,					__UpperCamelCase			:       Optional[Any]     ,					__UpperCamelCase			:       Dict     ,					__UpperCamelCase			:       Any     ,					__UpperCamelCase			:       List[str]     ,					__UpperCamelCase			:       Optional[Any]     ,					__UpperCamelCase			:       str     )		->List[Any]:






            '''simple docstring'''






            _UpperCAmelCase          =   TFMobileBertForMaskedLM(config=__UpperCamelCase     )
            _UpperCAmelCase          =   {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
            _UpperCAmelCase          =   model(__UpperCamelCase     )
            self.parent.assertEqual(result.logits.shape     ,					(self.batch_size, self.seq_length, self.vocab_size)     )







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






            '''simple docstring'''






            _UpperCAmelCase          =   TFMobileBertForNextSentencePrediction(config=__UpperCamelCase     )
            _UpperCAmelCase          =   {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
            _UpperCAmelCase          =   model(__UpperCamelCase     )
            self.parent.assertEqual(result.logits.shape     ,					(self.batch_size, 2)     )







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






            '''simple docstring'''






            _UpperCAmelCase          =   TFMobileBertForPreTraining(config=__UpperCamelCase     )
            _UpperCAmelCase          =   {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
            _UpperCAmelCase          =   model(__UpperCamelCase     )
            self.parent.assertEqual(
                result.prediction_logits.shape     ,					(self.batch_size, self.seq_length, self.vocab_size)     )
            self.parent.assertEqual(result.seq_relationship_logits.shape     ,					(self.batch_size, 2)     )







        def      _snake_case					(			self			:       Union[str, Any]     ,					__UpperCamelCase			:       Optional[int]     ,					__UpperCamelCase			:       List[Any]     ,					__UpperCamelCase			:       str     ,					__UpperCamelCase			:       Any     ,					__UpperCamelCase			:       Optional[int]     ,					__UpperCamelCase			:       Optional[Any]     ,					__UpperCamelCase			:       Optional[Any]     )		->Dict:






            '''simple docstring'''






            _UpperCAmelCase          =   self.num_labels
            _UpperCAmelCase          =   TFMobileBertForSequenceClassification(config=__UpperCamelCase     )
            _UpperCAmelCase          =   {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
            _UpperCAmelCase          =   model(__UpperCamelCase     )
            self.parent.assertEqual(result.logits.shape     ,					(self.batch_size, self.num_labels)     )







        def      _snake_case					(			self			:       List[Any]     ,					__UpperCamelCase			:       Any     ,					__UpperCamelCase			:       Union[str, Any]     ,					__UpperCamelCase			:       Optional[int]     ,					__UpperCamelCase			:       List[Any]     ,					__UpperCamelCase			:       Dict     ,					__UpperCamelCase			:       Optional[Any]     ,					__UpperCamelCase			:       Optional[int]     )		->Optional[int]:






            '''simple docstring'''






            _UpperCAmelCase          =   self.num_choices
            _UpperCAmelCase          =   TFMobileBertForMultipleChoice(config=__UpperCamelCase     )
            _UpperCAmelCase          =   tf.tile(tf.expand_dims(__UpperCamelCase     ,					1     )     ,					(1, self.num_choices, 1)     )
            _UpperCAmelCase          =   tf.tile(tf.expand_dims(__UpperCamelCase     ,					1     )     ,					(1, self.num_choices, 1)     )
            _UpperCAmelCase          =   tf.tile(tf.expand_dims(__UpperCamelCase     ,					1     )     ,					(1, self.num_choices, 1)     )
            _UpperCAmelCase          =   {
                """input_ids""": multiple_choice_inputs_ids,
                """attention_mask""": multiple_choice_input_mask,
                """token_type_ids""": multiple_choice_token_type_ids,
            }
            _UpperCAmelCase          =   model(__UpperCamelCase     )
            self.parent.assertEqual(result.logits.shape     ,					(self.batch_size, self.num_choices)     )







        def      _snake_case					(			self			:       Union[str, Any]     ,					__UpperCamelCase			:       Any     ,					__UpperCamelCase			:       Any     ,					__UpperCamelCase			:       str     ,					__UpperCamelCase			:       Optional[int]     ,					__UpperCamelCase			:       List[Any]     ,					__UpperCamelCase			:       Dict     ,					__UpperCamelCase			:       List[Any]     )		->str:






            '''simple docstring'''






            _UpperCAmelCase          =   self.num_labels
            _UpperCAmelCase          =   TFMobileBertForTokenClassification(config=__UpperCamelCase     )
            _UpperCAmelCase          =   {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
            _UpperCAmelCase          =   model(__UpperCamelCase     )
            self.parent.assertEqual(result.logits.shape     ,					(self.batch_size, self.seq_length, self.num_labels)     )







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






            '''simple docstring'''






            _UpperCAmelCase          =   TFMobileBertForQuestionAnswering(config=__UpperCamelCase     )
            _UpperCAmelCase          =   {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
            _UpperCAmelCase          =   model(__UpperCamelCase     )
            self.parent.assertEqual(result.start_logits.shape     ,					(self.batch_size, self.seq_length)     )
            self.parent.assertEqual(result.end_logits.shape     ,					(self.batch_size, self.seq_length)     )







        def      _snake_case					(			self			:       str     )		->int:






            '''simple docstring'''






            _UpperCAmelCase          =   self.prepare_config_and_inputs()
            (
                (
                _UpperCAmelCase
            )    ,(
                _UpperCAmelCase
            )    ,(
                _UpperCAmelCase
            )    ,(
                _UpperCAmelCase
            )    ,(
                _UpperCAmelCase
            )    ,(
                _UpperCAmelCase
            )    ,(
                _UpperCAmelCase
            )    ,
            )          =   config_and_inputs
            _UpperCAmelCase          =   {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
            return config, inputs_dict







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






        '''simple docstring'''






        _UpperCAmelCase          =   TFMobileBertModelTest.TFMobileBertModelTester(self     )
        _UpperCAmelCase          =   ConfigTester(self     ,					config_class=__UpperCamelCase     ,					hidden_size=37     )







    def      _snake_case					(			self			:       Any     )		->int:






        '''simple docstring'''






        self.config_tester.run_common_tests()







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






        '''simple docstring'''






        _UpperCAmelCase          =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_mobilebert_model(*__UpperCamelCase     )







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






        '''simple docstring'''






        _UpperCAmelCase          =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_mobilebert_for_masked_lm(*__UpperCamelCase     )







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






        '''simple docstring'''






        _UpperCAmelCase          =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_mobilebert_for_multiple_choice(*__UpperCamelCase     )







    def      _snake_case					(			self			:       int     )		->Any:






        '''simple docstring'''






        _UpperCAmelCase          =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*__UpperCamelCase     )







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






        '''simple docstring'''






        _UpperCAmelCase          =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_mobilebert_for_pretraining(*__UpperCamelCase     )







    def      _snake_case					(			self			:       Any     )		->int:






        '''simple docstring'''






        _UpperCAmelCase          =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_mobilebert_for_question_answering(*__UpperCamelCase     )







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






        '''simple docstring'''






        _UpperCAmelCase          =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_mobilebert_for_sequence_classification(*__UpperCamelCase     )







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






        '''simple docstring'''






        _UpperCAmelCase          =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_mobilebert_for_token_classification(*__UpperCamelCase     )







    @slow
    def      _snake_case					(			self			:       Optional[Any]     )		->Dict:






        '''simple docstring'''






        for model_name in ["google/mobilebert-uncased"]:
            _UpperCAmelCase          =   TFMobileBertModel.from_pretrained(__UpperCamelCase     )
            self.assertIsNotNone(__UpperCamelCase     )







@require_tf
class 		a_						( unittest.TestCase	):







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






        '''simple docstring'''






        _UpperCAmelCase          =   TFMobileBertForPreTraining.from_pretrained("""google/mobilebert-uncased"""     )
        _UpperCAmelCase          =   tf.constant([[0, 1, 2, 3, 4, 5]]     )
        _UpperCAmelCase          =   model(__UpperCamelCase     )[0]

        _UpperCAmelCase          =   [1, 6, 3_05_22]
        self.assertEqual(output.shape     ,					__UpperCamelCase     )

        _UpperCAmelCase          =   tf.constant(
            [
                [
                    [-4.5_9_1_9_5_4_7, -9.2_4_8_2_9_5, -9.6_4_5_2_5_6],
                    [-6.7_3_0_6_1_7_5, -6.4_4_0_2_8_4, -6.6_0_5_2_8_3_7],
                    [-7.2_7_4_3_5_0_6, -6.7_8_4_7_9_1_5, -6.0_2_4_6_7_3],
                ]
            ]     )
        tf.debugging.assert_near(output[:, :3, :3]     ,					__UpperCamelCase     ,					atol=1e-4     )

"""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()      )]      )