



"""simple docstring"""






import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
     import os

     # The slow tests are often failing with OOM error on GPU
     # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
     # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
     snake_case   					=				'''platform'''

     import jax
     import jax.numpy as jnp

     from transformers import BlenderbotTokenizer
     from transformers.models.blenderbot.modeling_flax_blenderbot import (
         FlaxBlenderbotForConditionalGeneration,
         FlaxBlenderbotModel,
         shift_tokens_right,
     )




def 			snake_case		(		lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_=None  ,						lowerCAmelCase_=None  ,						lowerCAmelCase_=None  ,						lowerCAmelCase_=None  ,						lowerCAmelCase_=None  ,						lowerCAmelCase_=None  ,						)						->				List[Any]:
  if attention_mask is None:
    _snake_case   	=      np.where(input_ids != config.pad_token_id  ,						1  ,						0	)
  if decoder_attention_mask is None:
    _snake_case   	=      np.where(decoder_input_ids != config.pad_token_id  ,						1  ,						0	)
  if head_mask is None:
    _snake_case   	=      np.ones((config.encoder_layers, config.encoder_attention_heads)	)
  if decoder_head_mask is None:
    _snake_case   	=      np.ones((config.decoder_layers, config.decoder_attention_heads)	)
  if cross_attn_head_mask is None:
    _snake_case   	=      np.ones((config.decoder_layers, config.decoder_attention_heads)	)
  return {
      "input_ids": input_ids,
      "decoder_input_ids": decoder_input_ids,
      "attention_mask": attention_mask,
      "decoder_attention_mask": attention_mask,
  }



class     UpperCAmelCase    :



   def __init__(       self    :	List[str]							,       __lowerCamelCase    :	str							,       __lowerCamelCase    :	List[Any]=1_3							,       __lowerCamelCase    :	int=7							,       __lowerCamelCase    :	Dict=True							,       __lowerCamelCase    :	List[str]=False							,       __lowerCamelCase    :	Optional[int]=9_9							,       __lowerCamelCase    :	Any=1_6							,       __lowerCamelCase    :	Tuple=2							,       __lowerCamelCase    :	Tuple=4							,       __lowerCamelCase    :	Optional[int]=4							,       __lowerCamelCase    :	Tuple="gelu"							,       __lowerCamelCase    :	int=0.1							,       __lowerCamelCase    :	Optional[int]=0.1							,       __lowerCamelCase    :	List[str]=3_2							,       __lowerCamelCase    :	Optional[int]=2							,       __lowerCamelCase    :	Optional[Any]=1							,       __lowerCamelCase    :	Dict=0							,       __lowerCamelCase    :	List[str]=0.0_2							,       ):



     """simple docstring"""
     _snake_case   	=      parent
     _snake_case   	=      batch_size
     _snake_case   	=      seq_length
     _snake_case   	=      is_training
     _snake_case   	=      use_labels
     _snake_case   	=      vocab_size
     _snake_case   	=      hidden_size
     _snake_case   	=      num_hidden_layers
     _snake_case   	=      num_attention_heads
     _snake_case   	=      intermediate_size
     _snake_case   	=      hidden_act
     _snake_case   	=      hidden_dropout_prob
     _snake_case   	=      attention_probs_dropout_prob
     _snake_case   	=      max_position_embeddings
     _snake_case   	=      eos_token_id
     _snake_case   	=      pad_token_id
     _snake_case   	=      bos_token_id
     _snake_case   	=      initializer_range



   def    __UpperCAmelCase    (       self    :	Optional[int]				):



     """simple docstring"""
     _snake_case   	=      np.clip(ids_tensor([self.batch_size, self.seq_length - 1]							,       self.vocab_size				)							,       3							,       self.vocab_size				)
     _snake_case   	=      np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)							,       dtype=np.intaa				))							,       -1				)

     _snake_case   	=      shift_tokens_right(__lowerCamelCase							,       1							,       2				)

     _snake_case   	=      BlenderbotConfig(
         vocab_size=self.vocab_size							,       d_model=self.hidden_size							,       encoder_layers=self.num_hidden_layers							,       decoder_layers=self.num_hidden_layers							,       encoder_attention_heads=self.num_attention_heads							,       decoder_attention_heads=self.num_attention_heads							,       encoder_ffn_dim=self.intermediate_size							,       decoder_ffn_dim=self.intermediate_size							,       dropout=self.hidden_dropout_prob							,       attention_dropout=self.attention_probs_dropout_prob							,       max_position_embeddings=self.max_position_embeddings							,       eos_token_id=self.eos_token_id							,       bos_token_id=self.bos_token_id							,       pad_token_id=self.pad_token_id							,       initializer_range=self.initializer_range							,       use_cache=__lowerCamelCase							,       )
     _snake_case   	=      prepare_blenderbot_inputs_dict(__lowerCamelCase							,       __lowerCamelCase							,       __lowerCamelCase				)
     return config, inputs_dict



   def    __UpperCAmelCase    (       self    :	Any				):



     """simple docstring"""
     _snake_case	,     _snake_case   	=      self.prepare_config_and_inputs()
     return config, inputs_dict



   def    __UpperCAmelCase    (       self    :	Optional[int]							,       __lowerCamelCase    :	Optional[Any]							,       __lowerCamelCase    :	List[Any]							,       __lowerCamelCase    :	Any				):



     """simple docstring"""
     _snake_case   	=      2_0
     _snake_case   	=      model_class_name(__lowerCamelCase				)

     _snake_case   	=      model.encode(inputs_dict['''input_ids''']				)

     _snake_case	,     _snake_case   	=      (
         inputs_dict['''decoder_input_ids'''],
         inputs_dict['''decoder_attention_mask'''],
     )

     _snake_case   	=      model.init_cache(decoder_input_ids.shape[0]							,       __lowerCamelCase							,       __lowerCamelCase				)
     _snake_case   	=      jnp.ones((decoder_input_ids.shape[0], max_decoder_length)							,       dtype='''i4'''				)

     _snake_case   	=      jnp.broadcast_to(
         jnp.arange(decoder_input_ids.shape[-1] - 1				)[None, :]							,       (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)							,       )
     _snake_case   	=      model.decode(
         decoder_input_ids[:, :-1]							,       __lowerCamelCase							,       decoder_attention_mask=__lowerCamelCase							,       past_key_values=__lowerCamelCase							,       decoder_position_ids=__lowerCamelCase							,       )

     _snake_case   	=      jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]							,       dtype='''i4'''				)
     _snake_case   	=      model.decode(
         decoder_input_ids[:, -1:]							,       __lowerCamelCase							,       decoder_attention_mask=__lowerCamelCase							,       past_key_values=outputs_cache.past_key_values							,       decoder_position_ids=__lowerCamelCase							,       )

     _snake_case   	=      model.decode(__lowerCamelCase							,       __lowerCamelCase				)

     _snake_case   	=      np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])				)				)
     self.parent.assertTrue(diff < 1E-3							,       msg=f"""Max diff is {diff}"""				)





   def    __UpperCAmelCase    (       self    :	Tuple							,       __lowerCamelCase    :	List[Any]							,       __lowerCamelCase    :	Any							,       __lowerCamelCase    :	str				):



     """simple docstring"""
     _snake_case   	=      2_0
     _snake_case   	=      model_class_name(__lowerCamelCase				)

     _snake_case   	=      model.encode(inputs_dict['''input_ids''']				)

     _snake_case	,     _snake_case   	=      (
         inputs_dict['''decoder_input_ids'''],
         inputs_dict['''decoder_attention_mask'''],
     )

     _snake_case   	=      jnp.concatenate(
         [
             decoder_attention_mask,
             jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])				),
         ]							,       axis=-1							,       )

     _snake_case   	=      model.init_cache(decoder_input_ids.shape[0]							,       __lowerCamelCase							,       __lowerCamelCase				)
     _snake_case   	=      jnp.broadcast_to(
         jnp.arange(decoder_input_ids.shape[-1] - 1				)[None, :]							,       (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)							,       )

     _snake_case   	=      model.decode(
         decoder_input_ids[:, :-1]							,       __lowerCamelCase							,       decoder_attention_mask=__lowerCamelCase							,       past_key_values=__lowerCamelCase							,       decoder_position_ids=__lowerCamelCase							,       )
     _snake_case   	=      jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]							,       dtype='''i4'''				)
     _snake_case   	=      model.decode(
         decoder_input_ids[:, -1:]							,       __lowerCamelCase							,       past_key_values=outputs_cache.past_key_values							,       decoder_attention_mask=__lowerCamelCase							,       decoder_position_ids=__lowerCamelCase							,       )

     _snake_case   	=      model.decode(__lowerCamelCase							,       __lowerCamelCase							,       decoder_attention_mask=__lowerCamelCase				)

     _snake_case   	=      np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])				)				)
     self.parent.assertTrue(diff < 1E-3							,       msg=f"""Max diff is {diff}"""				)



@require_flax
class     UpperCAmelCase    (	unittest.TestCase  ):
   A__		:	Dict           =  99



   def    __UpperCAmelCase    (       self    :	List[str]				):



     """simple docstring"""
     _snake_case   	=      np.array(
         [
             [7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2],
             [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2],
             [5, 9_7, 1_7, 3_9, 9_4, 4_0, 2],
             [7_6, 8_3, 9_4, 2_5, 7_0, 7_8, 2],
             [8_7, 5_9, 4_1, 3_5, 4_8, 6_6, 2],
             [5_5, 1_3, 1_6, 5_8, 5, 2, 1],  # note padding
             [6_4, 2_7, 3_1, 5_1, 1_2, 7_5, 2],
             [5_2, 6_4, 8_6, 1_7, 8_3, 3_9, 2],
             [4_8, 6_1, 9, 2_4, 7_1, 8_2, 2],
             [2_6, 1, 6_0, 4_8, 2_2, 1_3, 2],
             [2_1, 5, 6_2, 2_8, 1_4, 7_6, 2],
             [4_5, 9_8, 3_7, 8_6, 5_9, 4_8, 2],
             [7_0, 7_0, 5_0, 9, 2_8, 0, 2],
         ]							,       dtype=np.intaa							,       )

     _snake_case   	=      input_ids.shape[0]
     _snake_case   	=      BlenderbotConfig(
         vocab_size=self.vocab_size							,       d_model=2_4							,       encoder_layers=2							,       decoder_layers=2							,       encoder_attention_heads=2							,       decoder_attention_heads=2							,       encoder_ffn_dim=3_2							,       decoder_ffn_dim=3_2							,       max_position_embeddings=4_8							,       eos_token_id=2							,       pad_token_id=1							,       bos_token_id=0							,       )
     return config, input_ids, batch_size



   def    __UpperCAmelCase    (       self    :	Union[str, Any]				):



     """simple docstring"""
     _snake_case	,     _snake_case	,     _snake_case   	=      self._get_config_and_data()
     _snake_case   	=      FlaxBlenderbotForConditionalGeneration(__lowerCamelCase				)
     _snake_case   	=      lm_model(input_ids=__lowerCamelCase				)
     _snake_case   	=      (batch_size, input_ids.shape[1], config.vocab_size)
     self.assertEqual(outputs['''logits'''].shape							,       __lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	Union[str, Any]				):



     """simple docstring"""
     _snake_case   	=      BlenderbotConfig(
         vocab_size=self.vocab_size							,       d_model=1_4							,       encoder_layers=2							,       decoder_layers=2							,       encoder_attention_heads=2							,       decoder_attention_heads=2							,       encoder_ffn_dim=8							,       decoder_ffn_dim=8							,       max_position_embeddings=4_8							,       )
     _snake_case   	=      FlaxBlenderbotForConditionalGeneration(__lowerCamelCase				)
     _snake_case   	=      np.array([[7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2], [6_8, 3_4, 2_6, 5_8, 3_0, 2, 1]]							,       dtype=np.intaa				)
     _snake_case   	=      np.array([[8_2, 7_1, 8_2, 1_8, 2], [5_8, 6_8, 2, 1, 1]]							,       dtype=np.intaa				)
     _snake_case   	=      lm_model(input_ids=__lowerCamelCase							,       decoder_input_ids=__lowerCamelCase				)
     _snake_case   	=      (*summary.shape, config.vocab_size)
     self.assertEqual(outputs['''logits'''].shape							,       __lowerCamelCase				)





   def    __UpperCAmelCase    (       self    :	Optional[Any]				):



     """simple docstring"""
     _snake_case   	=      np.array([[7_1, 8_2, 1_8, 3_3, 2, 1, 1], [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2]]							,       dtype=np.intaa				)
     _snake_case   	=      shift_tokens_right(__lowerCamelCase							,       1							,       2				)
     _snake_case   	=      np.equal(__lowerCamelCase							,       1				).astype(np.floataa				).sum()
     _snake_case   	=      np.equal(__lowerCamelCase							,       1				).astype(np.floataa				).sum()
     self.assertEqual(shifted.shape							,       input_ids.shape				)
     self.assertEqual(__lowerCamelCase							,       n_pad_before - 1				)
     self.assertTrue(np.equal(shifted[:, 0]							,       2				).all()				)



@require_flax
class     UpperCAmelCase    (	__SCREAMING_SNAKE_CASE,unittest.TestCase,__SCREAMING_SNAKE_CASE  ):
   A__		:	Union[str, Any]           =  True
   A__		:	int           =  (
       (
           FlaxBlenderbotModel,
           FlaxBlenderbotForConditionalGeneration,
       )
       if is_flax_available()
       else ()
   )
   A__		:	List[Any]           =  (FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()



   def    __UpperCAmelCase    (       self    :	List[str]				):



     """simple docstring"""
     _snake_case   	=      FlaxBlenderbotModelTester(self				)



   def    __UpperCAmelCase    (       self    :	List[Any]				):



     """simple docstring"""
     _snake_case	,     _snake_case   	=      self.model_tester.prepare_config_and_inputs()
     for model_class in self.all_model_classes:
       self.model_tester.check_use_cache_forward(__lowerCamelCase							,       __lowerCamelCase							,       __lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	Optional[int]				):



     """simple docstring"""
     _snake_case	,     _snake_case   	=      self.model_tester.prepare_config_and_inputs()
     for model_class in self.all_model_classes:
       self.model_tester.check_use_cache_forward_with_attn_mask(__lowerCamelCase							,       __lowerCamelCase							,       __lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	Any				):



     """simple docstring"""
     _snake_case	,     _snake_case   	=      self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
       with self.subTest(model_class.__name__				):
         _snake_case   	=      self._prepare_for_class(__lowerCamelCase							,       __lowerCamelCase				)
         _snake_case   	=      model_class(__lowerCamelCase				)

         @jax.jit
         def encode_jitted(__lowerCamelCase    :	int							,       __lowerCamelCase    :	Optional[int]=None							,       **__lowerCamelCase    :	Union[str, Any]				):
           return model.encode(input_ids=__lowerCamelCase							,       attention_mask=__lowerCamelCase				)

         with self.subTest('''JIT Enabled'''				):
           _snake_case   	=      encode_jitted(**__lowerCamelCase				).to_tuple()

         with self.subTest('''JIT Disabled'''				):
           with jax.disable_jit():
             _snake_case   	=      encode_jitted(**__lowerCamelCase				).to_tuple()

         self.assertEqual(len(__lowerCamelCase				)							,       len(__lowerCamelCase				)				)
         for jitted_output, output in zip(__lowerCamelCase							,       __lowerCamelCase				):
           self.assertEqual(jitted_output.shape							,       output.shape				)



   def    __UpperCAmelCase    (       self    :	Optional[Any]				):



     """simple docstring"""
     _snake_case	,     _snake_case   	=      self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
       with self.subTest(model_class.__name__				):
         _snake_case   	=      model_class(__lowerCamelCase				)
         _snake_case   	=      model.encode(inputs_dict['''input_ids''']							,       inputs_dict['''attention_mask''']				)

         _snake_case   	=      {
             '''decoder_input_ids''': inputs_dict['''decoder_input_ids'''],
             '''decoder_attention_mask''': inputs_dict['''decoder_attention_mask'''],
             '''encoder_outputs''': encoder_outputs,
         }

         @jax.jit
         def decode_jitted(__lowerCamelCase    :	Optional[int]							,       __lowerCamelCase    :	Union[str, Any]							,       __lowerCamelCase    :	Optional[int]				):
           return model.decode(
               decoder_input_ids=__lowerCamelCase							,       decoder_attention_mask=__lowerCamelCase							,       encoder_outputs=__lowerCamelCase							,       )

         with self.subTest('''JIT Enabled'''				):
           _snake_case   	=      decode_jitted(**__lowerCamelCase				).to_tuple()

         with self.subTest('''JIT Disabled'''				):
           with jax.disable_jit():
             _snake_case   	=      decode_jitted(**__lowerCamelCase				).to_tuple()

         self.assertEqual(len(__lowerCamelCase				)							,       len(__lowerCamelCase				)				)
         for jitted_output, output in zip(__lowerCamelCase							,       __lowerCamelCase				):
           self.assertEqual(jitted_output.shape							,       output.shape				)



   @slow
   def    __UpperCAmelCase    (       self    :	Dict				):



     """simple docstring"""
     for model_class_name in self.all_model_classes:
       _snake_case   	=      model_class_name.from_pretrained('''facebook/blenderbot-400M-distill'''				)
       # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
       _snake_case   	=      np.ones((1, 1)				) * model.config.eos_token_id
       _snake_case   	=      model(__lowerCamelCase				)
       self.assertIsNotNone(__lowerCamelCase				)





   @unittest.skipUnless(jax_device != '''cpu'''							,       '''3B test too slow on CPU.'''				)
   @slow
   def    __UpperCAmelCase    (       self    :	List[str]				):



     """simple docstring"""
     _snake_case   	=      {'''num_beams''': 1, '''early_stopping''': True, '''min_length''': 1_5, '''max_length''': 2_5}
     _snake_case   	=      {'''skip_special_tokens''': True, '''clean_up_tokenization_spaces''': True}

     _snake_case   	=      FlaxBlenderbotForConditionalGeneration.from_pretrained('''facebook/blenderbot-3B'''							,       from_pt=__lowerCamelCase				)
     _snake_case   	=      BlenderbotTokenizer.from_pretrained('''facebook/blenderbot-3B'''				)

     _snake_case   	=      ['''Sam''']
     _snake_case   	=      tokenizer(__lowerCamelCase							,       return_tensors='''jax'''				)

     _snake_case   	=      model.generate(**__lowerCamelCase							,       **__lowerCamelCase				)
     _snake_case   	=      '''Sam is a great name. It means "sun" in Gaelic.'''

     _snake_case   	=      tokenizer.batch_decode(__lowerCamelCase							,       **__lowerCamelCase				)
     assert generated_txt[0].strip() == tgt_text








"""simple docstring"""

import io
import json

import fsspec
import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases



def       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__   )			->						Optional[Any]:
 assert isinstance(lowercase__					,					lowercase__   )
 assert dataset.num_rows == 4
 assert dataset.num_columns == 3
 assert dataset.column_names == ["col_1", "col_2", "col_3"]
 for feature, expected_dtype in expected_features.items():
  assert dataset.features[feature].dtype == expected_dtype



@pytest.mark.parametrize("keep_in_memory"					,					[False, True]   )
def       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__					,					lowercase__   )			->						Any:
 lowerCAmelCase__   :				Optional[int] 			=				tmp_path / "cache"
 lowerCAmelCase__   :				Dict 			=				{"col_1": "string", "col_2": "int64", "col_3": "float64"}
 with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
  lowerCAmelCase__   :				Tuple 			=				JsonDatasetReader(lowercase__					,					cache_dir=lowercase__					,					keep_in_memory=lowercase__   ).read()
 _check_json_dataset(lowercase__					,					lowercase__   )



@pytest.mark.parametrize(
    "features"					,					[
        None,
        {"col_1": "string", "col_2": "int64", "col_3": "float64"},
        {"col_1": "string", "col_2": "string", "col_3": "string"},
        {"col_1": "int32", "col_2": "int32", "col_3": "int32"},
        {"col_1": "float32", "col_2": "float32", "col_3": "float32"},
    ]					,					)
def       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__					,					lowercase__   )			->						Tuple:
 lowerCAmelCase__   :				Union[str, Any] 			=				tmp_path / "cache"
 lowerCAmelCase__   :				Tuple 			=				{"col_1": "string", "col_2": "int64", "col_3": "float64"}
 lowerCAmelCase__   :				List[Any] 			=				features.copy() if features else default_expected_features
 lowerCAmelCase__   :				int 			=				(
     Features({feature: Value(lowercase__   ) for feature, dtype in features.items()}   ) if features is not None else None
 )
 lowerCAmelCase__   :				Tuple 			=				JsonDatasetReader(lowercase__					,					features=lowercase__					,					cache_dir=lowercase__   ).read()
 _check_json_dataset(lowercase__					,					lowercase__   )



@pytest.mark.parametrize(
    "features"					,					[
        None,
        {"col_3": "float64", "col_1": "string", "col_2": "int64"},
    ]					,					)
def       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__					,					lowercase__   )			->						Optional[Any]:
 lowerCAmelCase__   :				Tuple 			=				tmp_path / "cache"
 lowerCAmelCase__   :				Optional[Any] 			=				{"col_3": "float64", "col_1": "string", "col_2": "int64"}
 lowerCAmelCase__   :				List[str] 			=				features.copy() if features else default_expected_features
 lowerCAmelCase__   :				Dict 			=				(
     Features({feature: Value(lowercase__   ) for feature, dtype in features.items()}   ) if features is not None else None
 )
 lowerCAmelCase__   :				Dict 			=				JsonDatasetReader(lowercase__					,					features=lowercase__					,					cache_dir=lowercase__   ).read()
 assert isinstance(lowercase__					,					lowercase__   )
 assert dataset.num_rows == 2
 assert dataset.num_columns == 3
 assert dataset.column_names == ["col_3", "col_1", "col_2"]
 for feature, expected_dtype in expected_features.items():
  assert dataset.features[feature].dtype == expected_dtype



def       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__   )			->						Tuple:
 # jsonl_312_path features are {"col_3": "float64", "col_1": "string", "col_2": "int64"}
 lowerCAmelCase__   :				Any 			=				{"col_2": "int64", "col_3": "float64", "col_1": "string"}
 lowerCAmelCase__   :				Union[str, Any] 			=				features.copy()
 lowerCAmelCase__   :				int 			=				(
     Features({feature: Value(lowercase__   ) for feature, dtype in features.items()}   ) if features is not None else None
 )
 lowerCAmelCase__   :				str 			=				tmp_path / "cache"
 lowerCAmelCase__   :				List[str] 			=				JsonDatasetReader(lowercase__					,					features=lowercase__					,					cache_dir=lowercase__   ).read()
 assert isinstance(lowercase__					,					lowercase__   )
 assert dataset.num_rows == 2
 assert dataset.num_columns == 3
 assert dataset.column_names == ["col_2", "col_3", "col_1"]
 for feature, expected_dtype in expected_features.items():
  assert dataset.features[feature].dtype == expected_dtype



@pytest.mark.parametrize("split"					,					[None, NamedSplit("train"   ), "train", "test"]   )
def       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__					,					lowercase__   )			->						Optional[Any]:
 lowerCAmelCase__   :				Optional[int] 			=				tmp_path / "cache"
 lowerCAmelCase__   :				List[Any] 			=				{"col_1": "string", "col_2": "int64", "col_3": "float64"}
 lowerCAmelCase__   :				Dict 			=				JsonDatasetReader(lowercase__					,					cache_dir=lowercase__					,					split=lowercase__   ).read()
 _check_json_dataset(lowercase__					,					lowercase__   )
 assert dataset.split == split if split else "train"



@pytest.mark.parametrize("path_type"					,					[str, list]   )
def       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__					,					lowercase__   )			->						Optional[Any]:
 if issubclass(lowercase__					,					lowercase__   ):
  lowerCAmelCase__   :				Dict 			=				jsonl_path
 elif issubclass(lowercase__					,					lowercase__   ):
  lowerCAmelCase__   :				Tuple 			=				[jsonl_path]
 lowerCAmelCase__   :				Any 			=				tmp_path / "cache"
 lowerCAmelCase__   :				List[str] 			=				{"col_1": "string", "col_2": "int64", "col_3": "float64"}
 lowerCAmelCase__   :				int 			=				JsonDatasetReader(lowercase__					,					cache_dir=lowercase__   ).read()
 _check_json_dataset(lowercase__					,					lowercase__   )



def       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__					,					lowercase__=("train",)   )			->						List[str]:
 assert isinstance(lowercase__					,					lowercase__   )
 for split in splits:
  lowerCAmelCase__   :				Dict 			=				dataset_dict[split]
  assert dataset.num_rows == 4
  assert dataset.num_columns == 3
  assert dataset.column_names == ["col_1", "col_2", "col_3"]
  for feature, expected_dtype in expected_features.items():
   assert dataset.features[feature].dtype == expected_dtype



@pytest.mark.parametrize("keep_in_memory"					,					[False, True]   )
def       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__					,					lowercase__   )			->						List[str]:
 lowerCAmelCase__   :				int 			=				tmp_path / "cache"
 lowerCAmelCase__   :				str 			=				{"col_1": "string", "col_2": "int64", "col_3": "float64"}
 with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
  lowerCAmelCase__   :				Dict 			=				JsonDatasetReader({"train": jsonl_path}					,					cache_dir=lowercase__					,					keep_in_memory=lowercase__   ).read()
 _check_json_datasetdict(lowercase__					,					lowercase__   )



@pytest.mark.parametrize(
    "features"					,					[
        None,
        {"col_1": "string", "col_2": "int64", "col_3": "float64"},
        {"col_1": "string", "col_2": "string", "col_3": "string"},
        {"col_1": "int32", "col_2": "int32", "col_3": "int32"},
        {"col_1": "float32", "col_2": "float32", "col_3": "float32"},
    ]					,					)
def       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__					,					lowercase__   )			->						List[str]:
 lowerCAmelCase__   :				str 			=				tmp_path / "cache"
 lowerCAmelCase__   :				Union[str, Any] 			=				{"col_1": "string", "col_2": "int64", "col_3": "float64"}
 lowerCAmelCase__   :				str 			=				features.copy() if features else default_expected_features
 lowerCAmelCase__   :				Dict 			=				(
     Features({feature: Value(lowercase__   ) for feature, dtype in features.items()}   ) if features is not None else None
 )
 lowerCAmelCase__   :				List[Any] 			=				JsonDatasetReader({"train": jsonl_path}					,					features=lowercase__					,					cache_dir=lowercase__   ).read()
 _check_json_datasetdict(lowercase__					,					lowercase__   )



@pytest.mark.parametrize("split"					,					[None, NamedSplit("train"   ), "train", "test"]   )
def       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__					,					lowercase__   )			->						Dict:
 if split:
  lowerCAmelCase__   :				int 			=				{split: jsonl_path}
 else:
  lowerCAmelCase__   :				str 			=				"train"
  lowerCAmelCase__   :				Tuple 			=				{"train": jsonl_path, "test": jsonl_path}
 lowerCAmelCase__   :				Any 			=				tmp_path / "cache"
 lowerCAmelCase__   :				Dict 			=				{"col_1": "string", "col_2": "int64", "col_3": "float64"}
 lowerCAmelCase__   :				int 			=				JsonDatasetReader(lowercase__					,					cache_dir=lowercase__   ).read()
 _check_json_datasetdict(lowercase__					,					lowercase__					,					splits=list(path.keys()   )   )
 assert all(dataset[split].split == split for split in path.keys()   )



def       SCREAMING_SNAKE_CASE							( lowercase__   )			->						List[Any]:
 return json.load(lowercase__   )



def       SCREAMING_SNAKE_CASE							( lowercase__   )			->						Optional[int]:
 return [json.loads(lowercase__   ) for line in buffer]






class 			__a					:





       """simple docstring"""


       @pytest.mark.parametrize("lines, load_json_function"			,					[(True, load_json_lines), (False, load_json)]   )
       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case			,					snake_case			,					snake_case   ):





        """simple docstring"""

        with io.BytesIO() as buffer:
         JsonDatasetWriter(snake_case			,					snake_case			,					lines=snake_case   ).write()
         buffer.seek(0   )
         lowerCAmelCase__   :				int 			=				load_json_function(snake_case   )
        assert isinstance(snake_case			,					snake_case   )
        assert isinstance(exported_content[0]			,					snake_case   )
        assert len(snake_case   ) == 10






       @pytest.mark.parametrize(
           "orient, container, keys, len_at"			,					[
               ("records", list, {"tokens", "labels", "answers", "id"}, None),
               ("split", dict, {"columns", "data"}, "data"),
               ("index", dict, set("0123456789"   ), None),
               ("columns", dict, {"tokens", "labels", "answers", "id"}, "tokens"),
               ("values", list, None, None),
               ("table", dict, {"schema", "data"}, "data"),
           ]			,					)
       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case			,					snake_case			,					snake_case			,					snake_case			,					snake_case   ):





        """simple docstring"""

        with io.BytesIO() as buffer:
         JsonDatasetWriter(snake_case			,					snake_case			,					lines=snake_case			,					orient=snake_case   ).write()
         buffer.seek(0   )
         lowerCAmelCase__   :				Optional[Any] 			=				load_json(snake_case   )
        assert isinstance(snake_case			,					snake_case   )
        if keys:
         if container is dict:
          assert exported_content.keys() == keys
         else:
          assert exported_content[0].keys() == keys
        else:
         assert not hasattr(snake_case			,					"keys"   ) and not hasattr(exported_content[0]			,					"keys"   )
        if len_at:
         assert len(exported_content[len_at]   ) == 10
        else:
         assert len(snake_case   ) == 10






       @pytest.mark.parametrize("lines, load_json_function"			,					[(True, load_json_lines), (False, load_json)]   )
       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case			,					snake_case			,					snake_case   ):





        """simple docstring"""

        with io.BytesIO() as buffer:
         JsonDatasetWriter(snake_case			,					snake_case			,					lines=snake_case			,					num_proc=2   ).write()
         buffer.seek(0   )
         lowerCAmelCase__   :				int 			=				load_json_function(snake_case   )
        assert isinstance(snake_case			,					snake_case   )
        assert isinstance(exported_content[0]			,					snake_case   )
        assert len(snake_case   ) == 10






       @pytest.mark.parametrize(
           "orient, container, keys, len_at"			,					[
               ("records", list, {"tokens", "labels", "answers", "id"}, None),
               ("split", dict, {"columns", "data"}, "data"),
               ("index", dict, set("0123456789"   ), None),
               ("columns", dict, {"tokens", "labels", "answers", "id"}, "tokens"),
               ("values", list, None, None),
               ("table", dict, {"schema", "data"}, "data"),
           ]			,					)
       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case			,					snake_case			,					snake_case			,					snake_case			,					snake_case   ):





        """simple docstring"""

        with io.BytesIO() as buffer:
         JsonDatasetWriter(snake_case			,					snake_case			,					lines=snake_case			,					orient=snake_case			,					num_proc=2   ).write()
         buffer.seek(0   )
         lowerCAmelCase__   :				Tuple 			=				load_json(snake_case   )
        assert isinstance(snake_case			,					snake_case   )
        if keys:
         if container is dict:
          assert exported_content.keys() == keys
         else:
          assert exported_content[0].keys() == keys
        else:
         assert not hasattr(snake_case			,					"keys"   ) and not hasattr(exported_content[0]			,					"keys"   )
        if len_at:
         assert len(exported_content[len_at]   ) == 10
        else:
         assert len(snake_case   ) == 10






       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case   ):





        """simple docstring"""

        with pytest.raises(snake_case   ):
         with io.BytesIO() as buffer:
          JsonDatasetWriter(snake_case			,					snake_case			,					num_proc=0   )






       @pytest.mark.parametrize("compression, extension"			,					[("gzip", "gz"), ("bz2", "bz2"), ("xz", "xz")]   )
       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case			,					snake_case			,					snake_case			,					snake_case			,					snake_case   ):





        """simple docstring"""

        lowerCAmelCase__   :				Any 			=				tmp_path_factory.mktemp("data"   ) / F"""test.json.{extension}"""
        lowerCAmelCase__   :				str 			=				str(shared_datadir / F"""test_file.json.{extension}"""   )
        JsonDatasetWriter(snake_case			,					snake_case			,					compression=snake_case   ).write()

        with fsspec.open(snake_case			,					"rb"			,					compression="infer"   ) as f:
         lowerCAmelCase__   :				Optional[int] 			=				f.read()
        with fsspec.open(snake_case			,					"rb"			,					compression="infer"   ) as f:
         lowerCAmelCase__   :				Optional[int] 			=				f.read()
        assert exported_content == original_content






