

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       a__					(		UpperCAmelCase							:      int    ,			UpperCAmelCase							:      List[Any]      )  ->						List[Any]:
  assert isinstance(lowerCAmelCase__    ,			lowerCAmelCase__      )
  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       a__					(		UpperCAmelCase							:      Optional[Any]    ,			UpperCAmelCase							:      str    ,			UpperCAmelCase							:      Tuple      )  ->						Optional[int]:
  UpperCAmelCase	:							int						=			tmp_path / '''cache'''
  UpperCAmelCase	:							List[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():
    UpperCAmelCase	:							Optional[int]						=			JsonDatasetReader(lowerCAmelCase__    ,			cache_dir=lowerCAmelCase__    ,			keep_in_memory=lowerCAmelCase__      ).read()
  _check_json_dataset(lowerCAmelCase__    ,			lowerCAmelCase__      )

@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       a__					(		UpperCAmelCase							:      Optional[int]    ,			UpperCAmelCase							:      Optional[Any]    ,			UpperCAmelCase							:      Any      )  ->						str:
  UpperCAmelCase	:							Tuple						=			tmp_path / '''cache'''
  UpperCAmelCase	:							Any						=			{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
  UpperCAmelCase	:							Union[str, Any]						=			features.copy() if features else default_expected_features
  UpperCAmelCase	:							Union[str, Any]						=			(
      Features({feature: Value(lowerCAmelCase__      ) for feature, dtype in features.items()}      ) if features is not None else None
  )
  UpperCAmelCase	:							List[Any]						=			JsonDatasetReader(lowerCAmelCase__    ,			features=lowerCAmelCase__    ,			cache_dir=lowerCAmelCase__      ).read()
  _check_json_dataset(lowerCAmelCase__    ,			lowerCAmelCase__      )

@pytest.mark.parametrize(
    '''features'''    ,			[
        None,
        {'''col_3''': '''float64''', '''col_1''': '''string''', '''col_2''': '''int64'''},
    ]    ,			)
def       a__					(		UpperCAmelCase							:      Dict    ,			UpperCAmelCase							:      Optional[Any]    ,			UpperCAmelCase							:      Optional[int]      )  ->						Optional[Any]:
  UpperCAmelCase	:							Tuple						=			tmp_path / '''cache'''
  UpperCAmelCase	:							Tuple						=			{'''col_3''': '''float64''', '''col_1''': '''string''', '''col_2''': '''int64'''}
  UpperCAmelCase	:							Optional[int]						=			features.copy() if features else default_expected_features
  UpperCAmelCase	:							str						=			(
      Features({feature: Value(lowerCAmelCase__      ) for feature, dtype in features.items()}      ) if features is not None else None
  )
  UpperCAmelCase	:							Dict						=			JsonDatasetReader(lowerCAmelCase__    ,			features=lowerCAmelCase__    ,			cache_dir=lowerCAmelCase__      ).read()
  assert isinstance(lowerCAmelCase__    ,			lowerCAmelCase__      )
  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       a__					(		UpperCAmelCase							:      Any    ,			UpperCAmelCase							:      Optional[Any]      )  ->						List[str]:
  UpperCAmelCase	:							str						=			{'''col_2''': '''int64''', '''col_3''': '''float64''', '''col_1''': '''string'''}
  UpperCAmelCase	:							Tuple						=			features.copy()
  UpperCAmelCase	:							int						=			(
      Features({feature: Value(lowerCAmelCase__      ) for feature, dtype in features.items()}      ) if features is not None else None
  )
  UpperCAmelCase	:							Dict						=			tmp_path / '''cache'''
  UpperCAmelCase	:							Dict						=			JsonDatasetReader(lowerCAmelCase__    ,			features=lowerCAmelCase__    ,			cache_dir=lowerCAmelCase__      ).read()
  assert isinstance(lowerCAmelCase__    ,			lowerCAmelCase__      )
  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       a__					(		UpperCAmelCase							:      Dict    ,			UpperCAmelCase							:      List[str]    ,			UpperCAmelCase							:      Optional[Any]      )  ->						Tuple:
  UpperCAmelCase	:							Optional[Any]						=			tmp_path / '''cache'''
  UpperCAmelCase	:							Optional[int]						=			{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
  UpperCAmelCase	:							Optional[int]						=			JsonDatasetReader(lowerCAmelCase__    ,			cache_dir=lowerCAmelCase__    ,			split=lowerCAmelCase__      ).read()
  _check_json_dataset(lowerCAmelCase__    ,			lowerCAmelCase__      )
  assert dataset.split == split if split else "train"

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

def       a__					(		UpperCAmelCase							:      Dict    ,			UpperCAmelCase							:      str    ,			UpperCAmelCase							:      Dict=("train",)      )  ->						Optional[Any]:
  assert isinstance(lowerCAmelCase__    ,			lowerCAmelCase__      )
  for split in splits:
    UpperCAmelCase	:							Union[str, Any]						=			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       a__					(		UpperCAmelCase							:      Tuple    ,			UpperCAmelCase							:      Tuple    ,			UpperCAmelCase							:      List[str]      )  ->						Dict:
  UpperCAmelCase	:							str						=			tmp_path / '''cache'''
  UpperCAmelCase	:							int						=			{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
  with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
    UpperCAmelCase	:							Dict						=			JsonDatasetReader({'''train''': jsonl_path}    ,			cache_dir=lowerCAmelCase__    ,			keep_in_memory=lowerCAmelCase__      ).read()
  _check_json_datasetdict(lowerCAmelCase__    ,			lowerCAmelCase__      )

@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       a__					(		UpperCAmelCase							:      str    ,			UpperCAmelCase							:      Tuple    ,			UpperCAmelCase							:      int      )  ->						str:
  UpperCAmelCase	:							str						=			tmp_path / '''cache'''
  UpperCAmelCase	:							Union[str, Any]						=			{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
  UpperCAmelCase	:							List[str]						=			features.copy() if features else default_expected_features
  UpperCAmelCase	:							Tuple						=			(
      Features({feature: Value(lowerCAmelCase__      ) for feature, dtype in features.items()}      ) if features is not None else None
  )
  UpperCAmelCase	:							Dict						=			JsonDatasetReader({'''train''': jsonl_path}    ,			features=lowerCAmelCase__    ,			cache_dir=lowerCAmelCase__      ).read()
  _check_json_datasetdict(lowerCAmelCase__    ,			lowerCAmelCase__      )

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

def       a__					(		UpperCAmelCase							:      List[str]      )  ->						Optional[int]:
  return json.load(lowerCAmelCase__      )

def       a__					(		UpperCAmelCase							:      Optional[Any]      )  ->						List[Any]:
  return [json.loads(lowerCAmelCase__      ) for line in buffer]






class  __UpperCAmelCase					:



   @pytest.mark.parametrize('''lines, load_json_function''',		[(True, load_json_lines), (False, load_json)]						)
   def 	__magic_name__   (							self							:					Union[str, Any],		__A							:					str,		__A							:					Optional[Any],		__A							:					str						):
     with io.BytesIO() as buffer:
       JsonDatasetWriter(A__,		A__,		lines=A__						).write()
       buffer.seek(0						)
       UpperCAmelCase	:							List[Any]						=			load_json_function(A__						)
     assert isinstance(A__,		A__						)
     assert isinstance(exported_content[0],		A__						)
     assert len(A__						) == 1_0



   @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 	__magic_name__   (							self							:					Any,		__A							:					Dict,		__A							:					List[Any],		__A							:					str,		__A							:					Any,		__A							:					str						):
     with io.BytesIO() as buffer:
       JsonDatasetWriter(A__,		A__,		lines=A__,		orient=A__						).write()
       buffer.seek(0						)
       UpperCAmelCase	:							str						=			load_json(A__						)
     assert isinstance(A__,		A__						)
     if keys:
       if container is dict:
         assert exported_content.keys() == keys
       else:
         assert exported_content[0].keys() == keys
     else:
       assert not hasattr(A__,		'''keys'''						) and not hasattr(exported_content[0],		'''keys'''						)
     if len_at:
       assert len(exported_content[len_at]						) == 1_0
     else:
       assert len(A__						) == 1_0



   @pytest.mark.parametrize('''lines, load_json_function''',		[(True, load_json_lines), (False, load_json)]						)
   def 	__magic_name__   (							self							:					List[str],		__A							:					Any,		__A							:					Optional[int],		__A							:					Optional[Any]						):
     with io.BytesIO() as buffer:
       JsonDatasetWriter(A__,		A__,		lines=A__,		num_proc=2						).write()
       buffer.seek(0						)
       UpperCAmelCase	:							Optional[int]						=			load_json_function(A__						)
     assert isinstance(A__,		A__						)
     assert isinstance(exported_content[0],		A__						)
     assert len(A__						) == 1_0



   @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 	__magic_name__   (							self							:					int,		__A							:					List[Any],		__A							:					Any,		__A							:					Any,		__A							:					Optional[Any],		__A							:					Dict						):
     with io.BytesIO() as buffer:
       JsonDatasetWriter(A__,		A__,		lines=A__,		orient=A__,		num_proc=2						).write()
       buffer.seek(0						)
       UpperCAmelCase	:							List[Any]						=			load_json(A__						)
     assert isinstance(A__,		A__						)
     if keys:
       if container is dict:
         assert exported_content.keys() == keys
       else:
         assert exported_content[0].keys() == keys
     else:
       assert not hasattr(A__,		'''keys'''						) and not hasattr(exported_content[0],		'''keys'''						)
     if len_at:
       assert len(exported_content[len_at]						) == 1_0
     else:
       assert len(A__						) == 1_0



   def 	__magic_name__   (							self							:					Optional[int],		__A							:					List[Any]						):
     with pytest.raises(A__						):
       with io.BytesIO() as buffer:
         JsonDatasetWriter(A__,		A__,		num_proc=0						)





   @pytest.mark.parametrize('''compression, extension''',		[('''gzip''', '''gz'''), ('''bz2''', '''bz2'''), ('''xz''', '''xz''')]						)
   def 	__magic_name__   (							self							:					Union[str, Any],		__A							:					Union[str, Any],		__A							:					Optional[int],		__A							:					str,		__A							:					Optional[int],		__A							:					str						):
     UpperCAmelCase	:							Optional[Any]						=			tmp_path_factory.mktemp('''data'''						) / F'''test.json.{extension}'''
     UpperCAmelCase	:							Dict						=			str(shared_datadir / F'''test_file.json.{extension}'''						)
     JsonDatasetWriter(A__,		A__,		compression=A__						).write()

     with fsspec.open(A__,		'''rb''',		compression='''infer'''						) as f:
       UpperCAmelCase	:							str						=			f.read()
     with fsspec.open(A__,		'''rb''',		compression='''infer'''						) as f:
       UpperCAmelCase	:							Optional[Any]						=			f.read()
     assert exported_content == original_content








import random
import unittest

import numpy as np

import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax


if is_flax_available():
     import os

     import jax.numpy as jnp
     from jax import jit

     from transformers import AutoTokenizer, FlaxAutoModelForCausalLM
     from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model

     _lowerCamelCase    :       Any        =							"0.12"  # assumed parallelism: 8


if is_torch_available():
     import torch

def       a__					(		UpperCAmelCase							:      int    ,			UpperCAmelCase							:      List[str]    ,			UpperCAmelCase							:      List[str]=None      )  ->						List[Any]:
  if rng is None:
    UpperCAmelCase	:							Dict						=			random.Random()

  UpperCAmelCase	:							Optional[Any]						=			1
  for dim in shape:
    total_dims *= dim

  UpperCAmelCase	:							List[str]						=			[]
  for _ in range(UpperCAmelCase      ):
    values.append(rng.randint(0    ,			vocab_size - 1      )      )

  UpperCAmelCase	:							List[str]						=			np.array(UpperCAmelCase    ,			dtype=jnp.intaa      ).reshape(UpperCAmelCase      )

  return output

def       a__					(		UpperCAmelCase							:      List[str]    ,			UpperCAmelCase							:      Optional[int]=None      )  ->						List[str]:
  UpperCAmelCase	:							Optional[int]						=			ids_tensor(UpperCAmelCase    ,			vocab_size=2    ,			rng=UpperCAmelCase      )
  # make sure that at least one token is attended to for each batch
  UpperCAmelCase	:							str						=			1
  return attn_mask





@require_flax
class  __UpperCAmelCase					:
   UpperCamelCase       					=					None
   UpperCamelCase       					=					()



   def 	__magic_name__   (							self							:					str						):
     UpperCAmelCase			,						UpperCAmelCase	:							Tuple						=			self.model_tester.prepare_config_and_inputs_for_common()

     # cut to half length & take max batch_size 3
     UpperCAmelCase	:							Optional[int]						=			2
     UpperCAmelCase	:							Dict						=			inputs['''input_ids'''].shape[-1] // 2
     UpperCAmelCase	:							Dict						=			inputs['''input_ids'''][:max_batch_size, :sequence_length]

     UpperCAmelCase	:							Optional[int]						=			jnp.ones_like(__A						)
     UpperCAmelCase	:							Optional[int]						=			attention_mask[:max_batch_size, :sequence_length]

     # generate max 5 tokens
     UpperCAmelCase	:							Optional[Any]						=			input_ids.shape[-1] + 5
     if config.eos_token_id is not None and config.pad_token_id is None:
       # hack to allow generate for models such as GPT2 as is done in `generate()`
       UpperCAmelCase	:							Optional[Any]						=			config.eos_token_id
     return config, input_ids, attention_mask, max_length



   @is_pt_flax_cross_test
   def 	__magic_name__   (							self							:					Union[str, Any]						):
     UpperCAmelCase			,						UpperCAmelCase			,						UpperCAmelCase			,						UpperCAmelCase	:							Any						=			self._get_input_ids_and_config()
     UpperCAmelCase	:							Optional[Any]						=			False
     UpperCAmelCase	:							Any						=			max_length
     UpperCAmelCase	:							List[Any]						=			0

     for model_class in self.all_generative_model_classes:
       UpperCAmelCase	:							Union[str, Any]						=			model_class(__A						)

       UpperCAmelCase	:							List[str]						=			model_class.__name__[4:]  # Skip the "Flax" at the beginning
       UpperCAmelCase	:							List[Any]						=			getattr(__A,		__A						)
       UpperCAmelCase	:							Union[str, Any]						=			pt_model_class(__A						).eval()
       UpperCAmelCase	:							Tuple						=			load_flax_weights_in_pytorch_model(__A,		flax_model.params						)

       UpperCAmelCase	:							Dict						=			flax_model.generate(__A						).sequences
       UpperCAmelCase	:							str						=			pt_model.generate(torch.tensor(__A,		dtype=torch.long						)						)

       if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
         UpperCAmelCase	:							Any						=			flax_generation_outputs[:, : pt_generation_outputs.shape[-1]]

       self.assertListEqual(pt_generation_outputs.numpy().tolist(),		flax_generation_outputs.tolist()						)



   def 	__magic_name__   (							self							:					Tuple						):
     UpperCAmelCase			,						UpperCAmelCase			,						UpperCAmelCase			,						UpperCAmelCase	:							Union[str, Any]						=			self._get_input_ids_and_config()
     UpperCAmelCase	:							str						=			False
     UpperCAmelCase	:							Dict						=			max_length

     for model_class in self.all_generative_model_classes:
       UpperCAmelCase	:							Union[str, Any]						=			model_class(__A						)

       UpperCAmelCase	:							Optional[int]						=			model.generate(__A						).sequences
       self.assertEqual(generation_outputs.shape[-1],		__A						)

       UpperCAmelCase	:							List[Any]						=			jit(model.generate						)
       UpperCAmelCase	:							Optional[Any]						=			jit_generate(__A						).sequences

       self.assertListEqual(generation_outputs.tolist(),		jit_generation_outputs.tolist()						)



   def 	__magic_name__   (							self							:					str						):
     UpperCAmelCase			,						UpperCAmelCase			,						UpperCAmelCase			,						UpperCAmelCase	:							List[Any]						=			self._get_input_ids_and_config()
     UpperCAmelCase	:							str						=			True
     UpperCAmelCase	:							Dict						=			max_length

     for model_class in self.all_generative_model_classes:
       UpperCAmelCase	:							Union[str, Any]						=			model_class(__A						)

       UpperCAmelCase	:							Optional[Any]						=			model.generate(__A						).sequences
       self.assertEqual(generation_outputs.shape[-1],		__A						)

       UpperCAmelCase	:							str						=			jit(model.generate						)
       UpperCAmelCase	:							List[Any]						=			jit_generate(__A						).sequences

       self.assertListEqual(generation_outputs.tolist(),		jit_generation_outputs.tolist()						)



   def 	__magic_name__   (							self							:					Union[str, Any]						):
     UpperCAmelCase			,						UpperCAmelCase			,						UpperCAmelCase			,						UpperCAmelCase	:							int						=			self._get_input_ids_and_config()
     UpperCAmelCase	:							Dict						=			False
     UpperCAmelCase	:							Union[str, Any]						=			max_length
     UpperCAmelCase	:							List[Any]						=			2

     for model_class in self.all_generative_model_classes:
       UpperCAmelCase	:							int						=			model_class(__A						)

       UpperCAmelCase	:							str						=			model.generate(__A						).sequences
       self.assertEqual(generation_outputs.shape[-1],		__A						)

       UpperCAmelCase	:							int						=			jit(model.generate						)
       UpperCAmelCase	:							Union[str, Any]						=			jit_generate(__A						).sequences

       self.assertListEqual(generation_outputs.tolist(),		jit_generation_outputs.tolist()						)



   def 	__magic_name__   (							self							:					List[str]						):
     UpperCAmelCase			,						UpperCAmelCase			,						UpperCAmelCase			,						UpperCAmelCase	:							Optional[Any]						=			self._get_input_ids_and_config()
     UpperCAmelCase	:							Any						=			False
     UpperCAmelCase	:							Optional[int]						=			max_length
     UpperCAmelCase	:							Optional[int]						=			2
     UpperCAmelCase	:							str						=			2

     for model_class in self.all_generative_model_classes:
       UpperCAmelCase	:							int						=			model_class(__A						)

       UpperCAmelCase	:							Optional[Any]						=			model.generate(__A						).sequences
       self.assertEqual(generation_outputs.shape[0],		input_ids.shape[0] * config.num_return_sequences						)



   def 	__magic_name__   (							self							:					Any						):
     UpperCAmelCase			,						UpperCAmelCase			,						UpperCAmelCase			,						UpperCAmelCase	:							str						=			self._get_input_ids_and_config()
     UpperCAmelCase	:							str						=			True
     UpperCAmelCase	:							Union[str, Any]						=			max_length
     UpperCAmelCase	:							Union[str, Any]						=			0.8
     UpperCAmelCase	:							str						=			1_0
     UpperCAmelCase	:							Any						=			0.3
     UpperCAmelCase	:							str						=			1
     UpperCAmelCase	:							Union[str, Any]						=			8
     UpperCAmelCase	:							Optional[Any]						=			9

     for model_class in self.all_generative_model_classes:
       UpperCAmelCase	:							int						=			model_class(__A						)

       UpperCAmelCase	:							List[Any]						=			model.generate(__A						).sequences
       self.assertEqual(generation_outputs.shape[-1],		__A						)

       UpperCAmelCase	:							Optional[int]						=			jit(model.generate						)
       UpperCAmelCase	:							Any						=			jit_generate(__A						).sequences

       self.assertListEqual(generation_outputs.tolist(),		jit_generation_outputs.tolist()						)



   def 	__magic_name__   (							self							:					List[Any]						):
     UpperCAmelCase			,						UpperCAmelCase			,						UpperCAmelCase			,						UpperCAmelCase	:							Dict						=			self._get_input_ids_and_config()
     UpperCAmelCase	:							Optional[Any]						=			max_length
     UpperCAmelCase	:							Tuple						=			1
     UpperCAmelCase	:							Optional[Any]						=			8
     UpperCAmelCase	:							Optional[int]						=			9

     for model_class in self.all_generative_model_classes:
       UpperCAmelCase	:							int						=			model_class(__A						)

       UpperCAmelCase	:							List[str]						=			model.generate(__A						).sequences
       self.assertEqual(generation_outputs.shape[-1],		__A						)

       UpperCAmelCase	:							Dict						=			jit(model.generate						)
       UpperCAmelCase	:							List[str]						=			jit_generate(__A						).sequences

       self.assertListEqual(generation_outputs.tolist(),		jit_generation_outputs.tolist()						)



   def 	__magic_name__   (							self							:					Optional[Any]						):
     UpperCAmelCase			,						UpperCAmelCase			,						UpperCAmelCase			,						UpperCAmelCase	:							Union[str, Any]						=			self._get_input_ids_and_config()
     UpperCAmelCase	:							List[str]						=			max_length
     UpperCAmelCase	:							Union[str, Any]						=			2
     UpperCAmelCase	:							List[Any]						=			1
     UpperCAmelCase	:							List[str]						=			8
     UpperCAmelCase	:							int						=			9

     for model_class in self.all_generative_model_classes:
       UpperCAmelCase	:							Optional[Any]						=			model_class(__A						)

       UpperCAmelCase	:							Union[str, Any]						=			model.generate(__A						).sequences
       self.assertEqual(generation_outputs.shape[-1],		__A						)

       UpperCAmelCase	:							List[str]						=			jit(model.generate						)
       UpperCAmelCase	:							Tuple						=			jit_generate(__A						).sequences

       self.assertListEqual(generation_outputs.tolist(),		jit_generation_outputs.tolist()						)



   def 	__magic_name__   (							self							:					str						):
     UpperCAmelCase			,						UpperCAmelCase			,						UpperCAmelCase			,						UpperCAmelCase	:							Optional[int]						=			self._get_input_ids_and_config()

     # pad attention mask on the left
     UpperCAmelCase	:							Union[str, Any]						=			attention_mask.at[(0, 0)].set(0						)

     UpperCAmelCase	:							Tuple						=			False
     UpperCAmelCase	:							str						=			max_length

     for model_class in self.all_generative_model_classes:
       UpperCAmelCase	:							Any						=			model_class(__A						)

       UpperCAmelCase	:							Union[str, Any]						=			model.generate(__A,		attention_mask=__A						).sequences
       self.assertEqual(generation_outputs.shape[-1],		__A						)

       UpperCAmelCase	:							List[Any]						=			jit(model.generate						)
       UpperCAmelCase	:							Optional[Any]						=			jit_generate(__A,		attention_mask=__A						).sequences

       self.assertListEqual(generation_outputs.tolist(),		jit_generation_outputs.tolist()						)



   def 	__magic_name__   (							self							:					Optional[int]						):
     UpperCAmelCase			,						UpperCAmelCase			,						UpperCAmelCase			,						UpperCAmelCase	:							Optional[int]						=			self._get_input_ids_and_config()

     # pad attention mask on the left
     UpperCAmelCase	:							Union[str, Any]						=			attention_mask.at[(0, 0)].set(0						)

     UpperCAmelCase	:							Union[str, Any]						=			True
     UpperCAmelCase	:							Dict						=			max_length

     for model_class in self.all_generative_model_classes:
       UpperCAmelCase	:							Any						=			model_class(__A						)

       UpperCAmelCase	:							Optional[Any]						=			model.generate(__A,		attention_mask=__A						).sequences
       self.assertEqual(generation_outputs.shape[-1],		__A						)

       UpperCAmelCase	:							Optional[Any]						=			jit(model.generate						)
       UpperCAmelCase	:							Optional[Any]						=			jit_generate(__A,		attention_mask=__A						).sequences

       self.assertListEqual(generation_outputs.tolist(),		jit_generation_outputs.tolist()						)





   def 	__magic_name__   (							self							:					Tuple						):
     UpperCAmelCase			,						UpperCAmelCase			,						UpperCAmelCase			,						UpperCAmelCase	:							Union[str, Any]						=			self._get_input_ids_and_config()

     # pad attention mask on the left
     UpperCAmelCase	:							Dict						=			attention_mask.at[(0, 0)].set(0						)

     UpperCAmelCase	:							Union[str, Any]						=			2
     UpperCAmelCase	:							str						=			max_length

     for model_class in self.all_generative_model_classes:
       UpperCAmelCase	:							str						=			model_class(__A						)

       UpperCAmelCase	:							int						=			model.generate(__A,		attention_mask=__A						).sequences
       self.assertEqual(generation_outputs.shape[-1],		__A						)

       UpperCAmelCase	:							Optional[Any]						=			jit(model.generate						)
       UpperCAmelCase	:							Dict						=			jit_generate(__A,		attention_mask=__A						).sequences

       self.assertListEqual(generation_outputs.tolist(),		jit_generation_outputs.tolist()						)






@require_flax
class  __UpperCAmelCase					(	unittest.TestCase						):



   def 	__magic_name__   (							self							:					str						):
     UpperCAmelCase	:							Union[str, Any]						=			AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-bert'''						)
     UpperCAmelCase	:							List[str]						=			FlaxAutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-bert-flax-only'''						)

     UpperCAmelCase	:							int						=			'''Hello world'''
     UpperCAmelCase	:							Optional[int]						=			tokenizer(__A,		return_tensors='''np'''						).input_ids

     # typos are quickly detected (the correct argument is `do_sample`)
     with self.assertRaisesRegex(__A,		'''do_samples'''						):
       model.generate(__A,		do_samples=__A						)

     # arbitrary arguments that will not be used anywhere are also not accepted
     with self.assertRaisesRegex(__A,		'''foo'''						):
       UpperCAmelCase	:							Any						=			{'''foo''': '''bar'''}
       model.generate(__A,		**__A						)





