

import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import MarianConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

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
      __a						:List[Any]            = 'platform'

      import jax
      import jax.numpy as jnp

      from transformers import MarianTokenizer
      from transformers.models.marian.modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, shift_tokens_right

def 			__snake_case	(   __UpperCamelCase  :   int							,__UpperCamelCase  :   Union[str, Any]							,__UpperCamelCase  :   Optional[int]=None							,__UpperCamelCase  :   Optional[Any]=None							,__UpperCamelCase  :   Optional[Any]=None							,__UpperCamelCase  :   Any=None							,__UpperCamelCase  :   Optional[Any]=None							,__UpperCamelCase  :   Tuple=None							,):



      """simple docstring"""

      if attention_mask is None:
            A_	     =							np.where(input_ids != config.pad_token_id							,1							,0						)
      if decoder_attention_mask is None:
            A_	     =							np.where(decoder_input_ids != config.pad_token_id							,1							,0						)
      if head_mask is None:
            A_	     =							np.ones((config.encoder_layers, config.encoder_attention_heads)						)
      if decoder_head_mask is None:
            A_	     =							np.ones((config.decoder_layers, config.decoder_attention_heads)						)
      if cross_attn_head_mask is None:
            A_	     =							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      _a				:




      """simple docstring"""

      def __init__(      self       :							Union[str, Any]	, UpperCAmelCase       :							str	, UpperCAmelCase       :							Union[str, Any]=13	, UpperCAmelCase       :							List[str]=7	, UpperCAmelCase       :							int=True	, UpperCAmelCase       :							List[Any]=False	, UpperCAmelCase       :							str=99	, UpperCAmelCase       :							Dict=16	, UpperCAmelCase       :							Union[str, Any]=2	, UpperCAmelCase       :							Union[str, Any]=4	, UpperCAmelCase       :							Tuple=4	, UpperCAmelCase       :							Optional[int]="gelu"	, UpperCAmelCase       :							str=0.1	, UpperCAmelCase       :							Optional[int]=0.1	, UpperCAmelCase       :							Dict=32	, UpperCAmelCase       :							Optional[int]=2	, UpperCAmelCase       :							List[Any]=1	, UpperCAmelCase       :							Optional[Any]=0	, UpperCAmelCase       :							str=0.02	, ):
            A_	     =							parent
            A_	     =							batch_size
            A_	     =							seq_length
            A_	     =							is_training
            A_	     =							use_labels
            A_	     =							vocab_size
            A_	     =							hidden_size
            A_	     =							num_hidden_layers
            A_	     =							num_attention_heads
            A_	     =							intermediate_size
            A_	     =							hidden_act
            A_	     =							hidden_dropout_prob
            A_	     =							attention_probs_dropout_prob
            A_	     =							max_position_embeddings
            A_	     =							eos_token_id
            A_	     =							pad_token_id
            A_	     =							bos_token_id
            A_	     =							initializer_range





      def 		__A  (      self       :							Tuple  ):
            A_	     =							np.clip(ids_tensor([self.batch_size, self.seq_length - 1]	, self.vocab_size  )	, 3	, self.vocab_size  )
            A_	     =							np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)	, dtype=np.intaa  ))	, -1  )

            A_	     =							shift_tokens_right(UpperCAmelCase	, 1	, 2  )

            A_	     =							MarianConfig(
                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=UpperCAmelCase	, )
            A_	     =							prepare_marian_inputs_dict(UpperCAmelCase	, UpperCAmelCase	, UpperCAmelCase  )
            return config, inputs_dict





      def 		__A  (      self       :							List[Any]  ):
            A_     ,    A_	     =							self.prepare_config_and_inputs()
            return config, inputs_dict





      def 		__A  (      self       :							int	, UpperCAmelCase       :							Optional[int]	, UpperCAmelCase       :							Optional[Any]	, UpperCAmelCase       :							str  ):
            A_	     =							20
            A_	     =							model_class_name(UpperCAmelCase  )

            A_	     =							model.encode(inputs_dict["input_ids"]  )

            A_     ,    A_	     =							(
                inputs_dict["decoder_input_ids"],
                inputs_dict["decoder_attention_mask"],
            )

            A_	     =							model.init_cache(decoder_input_ids.shape[0]	, UpperCAmelCase	, UpperCAmelCase  )
            A_	     =							jnp.ones((decoder_input_ids.shape[0], max_decoder_length)	, dtype="i4"  )

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

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

            A_	     =							model.decode(UpperCAmelCase	, UpperCAmelCase  )

            A_	     =							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 		__A  (      self       :							Dict	, UpperCAmelCase       :							List[str]	, UpperCAmelCase       :							Any	, UpperCAmelCase       :							int  ):
            A_	     =							20
            A_	     =							model_class_name(UpperCAmelCase  )

            A_	     =							model.encode(inputs_dict["input_ids"]  )

            A_     ,    A_	     =							(
                inputs_dict["decoder_input_ids"],
                inputs_dict["decoder_attention_mask"],
            )

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

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

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

            A_	     =							model.decode(UpperCAmelCase	, UpperCAmelCase	, decoder_attention_mask=UpperCAmelCase  )

            A_	     =							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      _a				( UpperCAmelCase_    ,							unittest.TestCase    ,							UpperCAmelCase_							):




      """simple docstring"""

      _lowerCamelCase					: Optional[int]					=					True
      _lowerCamelCase					: Optional[Any]					=					(FlaxMarianModel, FlaxMarianMTModel) if is_flax_available() else ()
      _lowerCamelCase					: List[Any]					=					(FlaxMarianMTModel,) if is_flax_available() else ()





      def 		__A  (      self       :							Optional[int]  ):
            A_	     =							FlaxMarianModelTester(self  )





      def 		__A  (      self       :							List[Any]  ):
            A_     ,    A_	     =							self.model_tester.prepare_config_and_inputs()
            for model_class in self.all_model_classes:
                  self.model_tester.check_use_cache_forward(UpperCAmelCase	, UpperCAmelCase	, UpperCAmelCase  )





      def 		__A  (      self       :							Any  ):
            A_     ,    A_	     =							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(UpperCAmelCase	, UpperCAmelCase	, UpperCAmelCase  )





      def 		__A  (      self       :							str  ):
            A_     ,    A_	     =							self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                  with self.subTest(model_class.__name__  ):
                        A_	     =							self._prepare_for_class(UpperCAmelCase	, UpperCAmelCase  )
                        A_	     =							model_class(UpperCAmelCase  )

                        @jax.jit
                        def encode_jitted(UpperCAmelCase       :							str	, UpperCAmelCase       :							Any=None	, **UpperCAmelCase       :							Tuple  ):
                              return model.encode(input_ids=UpperCAmelCase	, attention_mask=UpperCAmelCase  )

                        with self.subTest("JIT Enabled"  ):
                              A_	     =							encode_jitted(**UpperCAmelCase  ).to_tuple()

                        with self.subTest("JIT Disabled"  ):
                              with jax.disable_jit():
                                    A_	     =							encode_jitted(**UpperCAmelCase  ).to_tuple()

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





      def 		__A  (      self       :							Any  ):
            A_     ,    A_	     =							self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                  with self.subTest(model_class.__name__  ):
                        A_	     =							model_class(UpperCAmelCase  )
                        A_	     =							model.encode(inputs_dict["input_ids"]	, inputs_dict["attention_mask"]  )

                        A_	     =							{
                            "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(UpperCAmelCase       :							Union[str, Any]	, UpperCAmelCase       :							Optional[int]	, UpperCAmelCase       :							int  ):
                              return model.decode(
                                  decoder_input_ids=UpperCAmelCase	, decoder_attention_mask=UpperCAmelCase	, encoder_outputs=UpperCAmelCase	, )

                        with self.subTest("JIT Enabled"  ):
                              A_	     =							decode_jitted(**UpperCAmelCase  ).to_tuple()

                        with self.subTest("JIT Disabled"  ):
                              with jax.disable_jit():
                                    A_	     =							decode_jitted(**UpperCAmelCase  ).to_tuple()

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







      @slow
      def 		__A  (      self       :							int  ):
            for model_class_name in self.all_model_classes:
                  A_	     =							model_class_name.from_pretrained("Helsinki-NLP/opus-mt-en-de"  )
                  # FlaxMarianForSequenceClassification expects eos token in input_ids
                  A_	     =							np.ones((1, 1)  ) * model.config.eos_token_id
                  A_	     =							model(UpperCAmelCase  )
                  self.assertIsNotNone(UpperCAmelCase  )




@require_flax
@require_sentencepiece
@require_tokenizers
class      _a				( unittest.TestCase							):




      """simple docstring"""

      _lowerCamelCase					: Union[str, Any]					=					None
      _lowerCamelCase					: Optional[Any]					=					None





      @classmethod
      def 		__A  (      cls       :							Optional[Any]  ):
            A_	     =							f'''Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}'''
            return cls





      @cached_property
      def 		__A  (      self       :							List[str]  ):
            return MarianTokenizer.from_pretrained(self.model_name  )





      @property
      def 		__A  (      self       :							int  ):
            return self.tokenizer.eos_token_id





      @cached_property
      def 		__A  (      self       :							List[Any]  ):
            A_	     =							FlaxMarianMTModel.from_pretrained(self.model_name  )
            self.assertEqual(model.config.decoder_start_token_id	, model.config.pad_token_id  )
            return model





      def 		__A  (      self       :							Optional[int]	, **UpperCAmelCase       :							Optional[int]  ):
            A_	     =							self.translate_src_text(**UpperCAmelCase  )
            self.assertListEqual(self.expected_text	, UpperCAmelCase  )







      def 		__A  (      self       :							Any	, **UpperCAmelCase       :							Optional[Any]  ):
            A_	     =							self.tokenizer(self.src_text	, padding=UpperCAmelCase	, return_tensors="np"	, **UpperCAmelCase  )
            A_	     =							self.model.generate(
                model_inputs.input_ids	, attention_mask=model_inputs.attention_mask	, num_beams=2	, max_length=128	, ).sequences
            A_	     =							self.tokenizer.batch_decode(UpperCAmelCase	, skip_special_tokens=UpperCAmelCase  )
            return generated_words




@require_flax
@require_sentencepiece
@require_tokenizers
class      _a				( UpperCAmelCase_							):




      """simple docstring"""

      _lowerCamelCase					: Any					=					'en'
      _lowerCamelCase					: Dict					=					'fr'
      _lowerCamelCase					: Dict					=					[
          'I am a small frog.',
          'Now I can forget the 100 words of german that I know.',
      ]
      _lowerCamelCase					: Union[str, Any]					=					[
          'Je suis une petite grenouille.',
          'Maintenant, je peux oublier les 100 mots d\'allemand que je connais.',
      ]





      @slow
      def 		__A  (      self       :							Any  ):
            self._assert_generated_batch_equal_expected()




@require_flax
@require_sentencepiece
@require_tokenizers
class      _a				( UpperCAmelCase_							):




      """simple docstring"""

      _lowerCamelCase					: Optional[int]					=					'fr'
      _lowerCamelCase					: Dict					=					'en'
      _lowerCamelCase					: Any					=					[
          'Donnez moi le micro.',
          'Tom et Mary étaient assis à une table.',  # Accents
      ]
      _lowerCamelCase					: Any					=					[
          'Give me the microphone.',
          'Tom and Mary were sitting at a table.',
      ]





      @slow
      def 		__A  (      self       :							Union[str, Any]  ):
            self._assert_generated_batch_equal_expected()




@require_flax
@require_sentencepiece
@require_tokenizers
class      _a				( UpperCAmelCase_							):




      """simple docstring"""

      _lowerCamelCase					: Union[str, Any]					=					'mt'
      _lowerCamelCase					: Optional[Any]					=					'en'
      _lowerCamelCase					: int					=					['Billi messu b\'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem.']
      _lowerCamelCase					: int					=					['Touching gently, Jesus healed a man who was affected by the sad disease of leprosy.']





      @slow
      def 		__A  (      self       :							Dict  ):
            self._assert_generated_batch_equal_expected()




@require_flax
@require_sentencepiece
@require_tokenizers
class      _a				( UpperCAmelCase_							):




      """simple docstring"""

      _lowerCamelCase					: Union[str, Any]					=					'en'
      _lowerCamelCase					: Tuple					=					'de'
      _lowerCamelCase					: Tuple					=					[
          'I am a small frog.',
          'Now I can forget the 100 words of german that I know.',
          'Tom asked his teacher for advice.',
          'That\'s how I would do it.',
          'Tom really admired Mary\'s courage.',
          'Turn around and close your eyes.',
      ]
      _lowerCamelCase					: Any					=					[
          'Ich bin ein kleiner Frosch.',
          'Jetzt kann ich die 100 Wörter des Deutschen vergessen, die ich kenne.',
          'Tom bat seinen Lehrer um Rat.',
          'So würde ich das machen.',
          'Tom bewunderte Marias Mut wirklich.',
          'Drehen Sie sich um und schließen Sie die Augen.',
      ]





      @slow
      def 		__A  (      self       :							Dict  ):
            self._assert_generated_batch_equal_expected()




@require_flax
@require_sentencepiece
@require_tokenizers
class      _a				( UpperCAmelCase_							):




      """simple docstring"""

      _lowerCamelCase					: Optional[Any]					=					'en'
      _lowerCamelCase					: Dict					=					'zh'
      _lowerCamelCase					: List[str]					=					['My name is Wolfgang and I live in Berlin']
      _lowerCamelCase					: List[str]					=					['我叫沃尔夫冈 我住在柏林']





      @slow
      def 		__A  (      self       :							Optional[int]  ):
            self._assert_generated_batch_equal_expected()




@require_flax
@require_sentencepiece
@require_tokenizers
class      _a				( UpperCAmelCase_							):




      """simple docstring"""

      _lowerCamelCase					: List[Any]					=					'ru'
      _lowerCamelCase					: Union[str, Any]					=					'fr'
      _lowerCamelCase					: Dict					=					['Он показал мне рукопись своей новой пьесы.']
      _lowerCamelCase					: Dict					=					['Il m\'a montré le manuscrit de sa nouvelle pièce.']





      @slow
      def 		__A  (      self       :							str  ):
            self._assert_generated_batch_equal_expected()




@require_flax
@require_sentencepiece
@require_tokenizers
class      _a				( UpperCAmelCase_							):




      """simple docstring"""

      _lowerCamelCase					: Tuple					=					'en'
      _lowerCamelCase					: Any					=					'ROMANCE'
      _lowerCamelCase					: Optional[int]					=					[
          '>>fr<< Don\'t spend so much time watching TV.',
          '>>pt<< Your message has been sent.',
          '>>es<< He\'s two years older than me.',
      ]
      _lowerCamelCase					: Union[str, Any]					=					[
          'Ne passez pas autant de temps à regarder la télé.',
          'A sua mensagem foi enviada.',
          'Es dos años más viejo que yo.',
      ]





      @slow
      def 		__A  (      self       :							Optional[Any]  ):
            self._assert_generated_batch_equal_expected()








import json
import os
import tempfile

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features import ArrayaD
from utils import generate_examples, get_duration


__a						:Dict            = (30, 487)
__a						:int            = (36, 1024)
__a						:Tuple            = (100, 100)
__a						:Dict            = 100

__a						:List[str]            = datasets.Features(
    {'text': ArrayaD(SHAPE_TEST_1, dtype='float32'), 'image': ArrayaD(SHAPE_TEST_2, dtype='float32')}
)

__a      ,			__a						:str            = os.path.split(__file__)
__a						:Optional[Any]            = os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))

@get_duration
def 			__snake_case	(   __UpperCamelCase  :   Optional[int]							,__UpperCamelCase  :   Union[str, Any]							,__UpperCamelCase  :   Dict						):



      """simple docstring"""

      with ArrowWriter(features=__UpperCamelCase							,path=os.path.join(__UpperCamelCase							,"beta.arrow"						)						) as writer:
            for key, record in dummy_data:
                  A_	     =							my_features.encode_example(__UpperCamelCase						)
                  writer.write(__UpperCamelCase						)
            A_     ,    A_	     =							writer.finalize()

@get_duration
def 			__snake_case	(   __UpperCamelCase  :   int							,__UpperCamelCase  :   Any						):



      """simple docstring"""

      A_	     =							datasets.Dataset.from_file(
          filename=os.path.join(__UpperCamelCase							,"beta.arrow"						)							,info=datasets.DatasetInfo(features=__UpperCamelCase						)						)
      for _ in dataset:
            pass

@get_duration
def 			__snake_case	(   __UpperCamelCase  :   Optional[Any]							,__UpperCamelCase  :   List[str]						):



      """simple docstring"""

      A_	     =							datasets.Dataset.from_file(
          filename=os.path.join(__UpperCamelCase							,"beta.arrow"						)							,info=datasets.DatasetInfo(features=__UpperCamelCase						)						)
      dataset.set_format("numpy"						)
      for _ in dataset:
            pass

@get_duration
def 			__snake_case	(   __UpperCamelCase  :   List[str]							,__UpperCamelCase  :   Tuple						):



      """simple docstring"""

      A_	     =							10
      A_	     =							datasets.Dataset.from_file(
          filename=os.path.join(__UpperCamelCase							,"beta.arrow"						)							,info=datasets.DatasetInfo(features=__UpperCamelCase						)						)
      for i in range(0							,len(__UpperCamelCase						)							,__UpperCamelCase						):
            A_	     =							dataset[i : i + batch_size]

@get_duration
def 			__snake_case	(   __UpperCamelCase  :   int							,__UpperCamelCase  :   List[Any]						):



      """simple docstring"""

      A_	     =							10
      A_	     =							datasets.Dataset.from_file(
          filename=os.path.join(__UpperCamelCase							,"beta.arrow"						)							,info=datasets.DatasetInfo(features=__UpperCamelCase						)						)
      dataset.set_format("numpy"						)
      for i in range(0							,len(__UpperCamelCase						)							,__UpperCamelCase						):
            A_	     =							dataset[i : i + batch_size]

@get_duration
def 			__snake_case	(   __UpperCamelCase  :   int							,__UpperCamelCase  :   Tuple						):



      """simple docstring"""

      A_	     =							datasets.Dataset.from_file(
          filename=os.path.join(__UpperCamelCase							,"beta.arrow"						)							,info=datasets.DatasetInfo(features=__UpperCamelCase						)						)
      for col in feats:
            A_	     =							dataset[col]

@get_duration
def 			__snake_case	(   __UpperCamelCase  :   Optional[int]							,__UpperCamelCase  :   Optional[Any]						):



      """simple docstring"""

      A_	     =							datasets.Dataset.from_file(
          filename=os.path.join(__UpperCamelCase							,"beta.arrow"						)							,info=datasets.DatasetInfo(features=__UpperCamelCase						)						)
      dataset.set_format("numpy"						)
      for col in feats:
            A_	     =							dataset[col]

def 			__snake_case	(   ):



      """simple docstring"""

      A_	     =							{}
      A_	     =							(
          read_unformated,
          read_formatted_as_numpy,
          read_batch_unformated,
          read_batch_formatted_as_numpy,
          read_col_unformated,
          read_col_formatted_as_numpy,
      )
      with tempfile.TemporaryDirectory() as tmp_dir:
            A_	     =							datasets.Features({"image": ArrayaD(__UpperCamelCase							,dtype="float32"						)}						)
            A_	     =							generate_examples(features=__UpperCamelCase							,num_examples=__UpperCamelCase						)
            A_	     =							write(__UpperCamelCase							,__UpperCamelCase							,__UpperCamelCase						)
            for read_func in read_functions:
                  A_	     =							read_func(__UpperCamelCase							,__UpperCamelCase						)

      with tempfile.TemporaryDirectory() as tmp_dir:
            # don't use fixed length for fair comparison
            # feats = datasets.Features(
            #     {"image": datasets.Sequence(datasets.Sequence(datasets.Value("float32"), SPEED_TEST_SHAPE[1]), SPEED_TEST_SHAPE[0])}
            # )
            A_	     =							datasets.Features({"image": datasets.Sequence(datasets.Sequence(datasets.Value("float32"						)						)						)}						)
            A_	     =							generate_examples(
                features=__UpperCamelCase							,num_examples=__UpperCamelCase							,seq_shapes={"image": SPEED_TEST_SHAPE}						)
            A_	     =							write(__UpperCamelCase							,__UpperCamelCase							,__UpperCamelCase						)
            for read_func in read_functions:
                  A_	     =							read_func(__UpperCamelCase							,__UpperCamelCase						)

      with tempfile.TemporaryDirectory() as tmp_dir:
            # don't use fixed length for fair comparison
            # feats = datasets.Features(
            #     {"image": datasets.Sequence(datasets.Value("float32"), SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1])}
            # )
            A_	     =							datasets.Features({"image": datasets.Sequence(datasets.Value("float32"						)						)}						)
            A_	     =							generate_examples(
                features=__UpperCamelCase							,num_examples=__UpperCamelCase							,seq_shapes={"image": [SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1]]}							,)
            A_	     =							write(__UpperCamelCase							,__UpperCamelCase							,__UpperCamelCase						)
            for read_func in read_functions:
                  A_	     =							read_func(__UpperCamelCase							,__UpperCamelCase						)

      with open(__UpperCamelCase							,"wb"						) as f:
            f.write(json.dumps(__UpperCamelCase						).encode("utf-8"						)						)


if __name__ == "__main__":  # useful to run the profiler
      benchmark_array_xd()