





import gc
import unittest

import numpy as np
import torch

from diffusers import DDPMScheduler, MidiProcessor, SpectrogramDiffusionPipeline
from diffusers.pipelines.spectrogram_diffusion import SpectrogramContEncoder, SpectrogramNotesEncoder, TaFilmDecoder
from diffusers.utils import require_torch_gpu, skip_mps, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_note_seq, require_onnxruntime

from ..pipeline_params import TOKENS_TO_AUDIO_GENERATION_BATCH_PARAMS, TOKENS_TO_AUDIO_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()


_UpperCamelCase   :							Any     =	"""./tests/fixtures/elise_format0.mid"""







@unittest.skip('''The note-seq package currently throws an error on import''')
class 						_lowerCAmelCase(						_a		,   unittest.TestCase):

  """simple docstring"""







  lowerCamelCase__      					=   SpectrogramDiffusionPipeline
  lowerCamelCase__      					=   PipelineTesterMixin.required_optional_params - {
      '''callback''',
      '''latents''',
      '''callback_steps''',
      '''output_type''',
      '''num_images_per_prompt''',
  }
  lowerCamelCase__      					=   False
  lowerCamelCase__      					=   False
  lowerCamelCase__      					=   TOKENS_TO_AUDIO_GENERATION_PARAMS
  lowerCamelCase__      					=   TOKENS_TO_AUDIO_GENERATION_BATCH_PARAMS

  def 		SCREAMING_SNAKE_CASE__	( self     )-> str:
     torch.manual_seed(0     )
     __A				 =       SpectrogramNotesEncoder(
         max_length=20_48  ,    vocab_size=15_36  ,    d_model=7_68  ,    dropout_rate=0.1  ,    num_layers=1  ,    num_heads=1  ,    d_kv=4  ,    d_ff=20_48  ,    feed_forward_proj='''gated-gelu'''  ,    )

     __A				 =       SpectrogramContEncoder(
         input_dims=1_28  ,    targets_context_length=2_56  ,    d_model=7_68  ,    dropout_rate=0.1  ,    num_layers=1  ,    num_heads=1  ,    d_kv=4  ,    d_ff=20_48  ,    feed_forward_proj='''gated-gelu'''  ,    )

     __A				 =       TaFilmDecoder(
         input_dims=1_28  ,    targets_length=2_56  ,    max_decoder_noise_time=20000.0  ,    d_model=7_68  ,    num_layers=1  ,    num_heads=1  ,    d_kv=4  ,    d_ff=20_48  ,    dropout_rate=0.1  ,    )

     __A				 =       DDPMScheduler()

     __A				 =       {
         '''notes_encoder''': notes_encoder.eval(),
         '''continuous_encoder''': continuous_encoder.eval(),
         '''decoder''': decoder.eval(),
         '''scheduler''': scheduler,
         '''melgan''': None,
     }
     return components

  def 		SCREAMING_SNAKE_CASE__	( self  ,    UpperCAmelCase  ,    UpperCAmelCase=0     )-> Any:
     if str(UpperCAmelCase     ).startswith('''mps'''     ):
        __A				 =       torch.manual_seed(UpperCAmelCase     )
     else:
        __A				 =       torch.Generator(device=UpperCAmelCase     ).manual_seed(UpperCAmelCase     )
     __A				 =       {
         '''input_tokens''': [
             [11_34, 90, 11_35, 11_33, 10_80, 1_12, 11_32, 10_80, 11_33, 10_79, 1_33, 11_32, 10_79, 11_33, 1] + [0] * 20_33
         ],
         '''generator''': generator,
         '''num_inference_steps''': 4,
         '''output_type''': '''mel''',
     }
     return inputs

  def 		SCREAMING_SNAKE_CASE__	( self     )-> Dict:
     __A				 =       '''cpu'''  # ensure determinism for the device-dependent torch.Generator
     __A				 =       self.get_dummy_components()
     __A				 =       SpectrogramDiffusionPipeline(**UpperCAmelCase     )
     __A				 =       pipe.to(UpperCAmelCase     )
     pipe.set_progress_bar_config(disable=UpperCAmelCase     )

     __A				 =       self.get_dummy_inputs(UpperCAmelCase     )
     __A				 =       pipe(**UpperCAmelCase     )
     __A				 =       output.audios

     __A				 =       mel[0, -3:, -3:]

     assert mel_slice.shape == (3, 3)
     __A				 =       np.array(
         [-11.512925, -4.788215, -0.46172905, -2.051715, -10.539147, -10.970963, -9.091634, 4.0, 4.0]     )
     assert np.abs(mel_slice.flatten() - expected_slice     ).max() < 1e-2

  @skip_mps
  def 		SCREAMING_SNAKE_CASE__	( self     )-> str:
     return super().test_save_load_local()

  @skip_mps
  def 		SCREAMING_SNAKE_CASE__	( self     )-> List[Any]:
     return super().test_dict_tuple_outputs_equivalent()

  @skip_mps
  def 		SCREAMING_SNAKE_CASE__	( self     )-> str:
     return super().test_save_load_optional_components()

  @skip_mps
  def 		SCREAMING_SNAKE_CASE__	( self     )-> List[Any]:
     return super().test_attention_slicing_forward_pass()

  def 		SCREAMING_SNAKE_CASE__	( self     )-> Optional[Any]:
     pass

  def 		SCREAMING_SNAKE_CASE__	( self     )-> Optional[Any]:
     pass





  @skip_mps
  def 		SCREAMING_SNAKE_CASE__	( self     )-> Optional[int]:
     return super().test_progress_bar()







@slow
@require_torch_gpu
@require_onnxruntime
@require_note_seq
class 						_lowerCAmelCase(						unittest.TestCase):

  """simple docstring"""







  def 		SCREAMING_SNAKE_CASE__	( self     )-> int:
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()

  def 		SCREAMING_SNAKE_CASE__	( self     )-> Tuple:
     # TODO - test that pipeline can decode tokens in a callback
     # so that music can be played live
     __A				 =       torch_device

     __A				 =       SpectrogramDiffusionPipeline.from_pretrained('''google/music-spectrogram-diffusion'''     )
     __A				 =       pipe.melgan
     __A				 =       None

     __A				 =       pipe.to(UpperCAmelCase     )
     pipe.set_progress_bar_config(disable=UpperCAmelCase     )

     def callback(UpperCAmelCase  ,    UpperCAmelCase     ):
        # decode mel to audio
        __A				 =       melgan(input_features=mel_output.astype(np.floataa     )     )[0]
        assert len(audio[0]     ) == 8_19_20 * (step + 1)
        # simulate that audio is played
        return audio

     __A				 =       MidiProcessor()
     __A				 =       processor(UpperCAmelCase     )

     __A				 =       input_tokens[:3]
     __A				 =       torch.manual_seed(0     )
     pipe(UpperCAmelCase  ,    num_inference_steps=5  ,    generator=UpperCAmelCase  ,    callback=UpperCAmelCase  ,    output_type='''mel'''     )

  def 		SCREAMING_SNAKE_CASE__	( self     )-> Tuple:
     __A				 =       torch_device

     __A				 =       SpectrogramDiffusionPipeline.from_pretrained('''google/music-spectrogram-diffusion'''     )
     __A				 =       pipe.to(UpperCAmelCase     )
     pipe.set_progress_bar_config(disable=UpperCAmelCase     )
     __A				 =       MidiProcessor()

     __A				 =       processor(UpperCAmelCase     )
     # just run two denoising loops
     __A				 =       input_tokens[:2]

     __A				 =       torch.manual_seed(0     )
     __A				 =       pipe(UpperCAmelCase  ,    num_inference_steps=2  ,    generator=UpperCAmelCase     )

     __A				 =       output.audios[0]

     assert abs(np.abs(UpperCAmelCase     ).sum() - 3612.841     ) < 1e-1





  def 		SCREAMING_SNAKE_CASE__	( self     )-> Any:
     __A				 =       torch_device

     __A				 =       SpectrogramDiffusionPipeline.from_pretrained('''google/music-spectrogram-diffusion'''     )
     __A				 =       pipe.to(UpperCAmelCase     )
     pipe.set_progress_bar_config(disable=UpperCAmelCase     )

     __A				 =       MidiProcessor()

     __A				 =       processor(UpperCAmelCase     )

     # just run 4 denoising loops
     __A				 =       input_tokens[:4]

     __A				 =       torch.manual_seed(0     )
     __A				 =       pipe(UpperCAmelCase  ,    num_inference_steps=1_00  ,    generator=UpperCAmelCase     )

     __A				 =       output.audios[0]
     assert abs(np.abs(UpperCAmelCase     ).sum() - 9389.1111     ) < 5e-2








import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import OPTConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, 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
    _UpperCamelCase   :							Dict     =	"""platform"""

    import jax
    import jax.numpy as jnp

    from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer
def 				__UpperCamelCase  (		snake_case    ,	snake_case    ,	snake_case=None    ,	snake_case=None     )  ->						str:


   '''simple docstring'''







   if attention_mask is None:
      __A				 =       np.where(input_ids != config.pad_token_id    ,	1    ,	0     )
   return {
       "input_ids": input_ids,
       "attention_mask": attention_mask,
   }







@require_flax
class 						_lowerCAmelCase:

  """simple docstring"""







  def __init__( self  ,    UpperCAmelCase  ,    UpperCAmelCase=13  ,    UpperCAmelCase=7  ,    UpperCAmelCase=True  ,    UpperCAmelCase=False  ,    UpperCAmelCase=99  ,    UpperCAmelCase=16  ,    UpperCAmelCase=2  ,    UpperCAmelCase=4  ,    UpperCAmelCase=4  ,    UpperCAmelCase="gelu"  ,    UpperCAmelCase=0.1  ,    UpperCAmelCase=0.1  ,    UpperCAmelCase=20  ,    UpperCAmelCase=2  ,    UpperCAmelCase=1  ,    UpperCAmelCase=0  ,    UpperCAmelCase=16  ,    UpperCAmelCase=16  ,    UpperCAmelCase=0.02  ,    )-> List[str]:
     __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				 =       embed_dim
     __A				 =       word_embed_proj_dim
     __A				 =       initializer_range
     __A				 =       False

  def 		SCREAMING_SNAKE_CASE__	( self     )-> Any:
     __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				 =       OPTConfig(
         vocab_size=self.vocab_size  ,    hidden_size=self.hidden_size  ,    num_hidden_layers=self.num_hidden_layers  ,    num_attention_heads=self.num_attention_heads  ,    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  ,    embed_dim=self.embed_dim  ,    is_encoder_decoder=UpperCAmelCase  ,    word_embed_proj_dim=self.word_embed_proj_dim  ,    initializer_range=self.initializer_range  ,    use_cache=UpperCAmelCase  ,    )
     __A				 =       prepare_opt_inputs_dict(UpperCAmelCase  ,    UpperCAmelCase     )
     return config, inputs_dict

  def 		SCREAMING_SNAKE_CASE__	( self     )-> int:
     __A ,						__A				 =       self.prepare_config_and_inputs()
     return config, inputs_dict

  def 		SCREAMING_SNAKE_CASE__	( self  ,    UpperCAmelCase  ,    UpperCAmelCase  ,    UpperCAmelCase     )-> Any:
     __A				 =       20
     __A				 =       model_class_name(UpperCAmelCase     )

     __A				 =       inputs_dict['''input_ids''']
     __A				 =       inputs_dict['''attention_mask''']

     __A				 =       model.init_cache(input_ids.shape[0]  ,    UpperCAmelCase     )
     __A				 =       jnp.ones((input_ids.shape[0], max_length)  ,    dtype='''i4'''     )

     __A				 =       jnp.broadcast_to(
         jnp.arange(input_ids.shape[-1] - 1     )[None, :]  ,    (input_ids.shape[0], input_ids.shape[-1] - 1)  ,    )
     __A				 =       model(
         input_ids[:, :-1]  ,    attention_mask=UpperCAmelCase  ,    past_key_values=UpperCAmelCase  ,    position_ids=UpperCAmelCase  ,    )

     __A				 =       jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]  ,    dtype='''i4'''     )
     __A				 =       model(
         input_ids[:, -1:]  ,    attention_mask=UpperCAmelCase  ,    past_key_values=outputs_cache.past_key_values  ,    position_ids=UpperCAmelCase  ,    )

     __A				 =       model(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 		SCREAMING_SNAKE_CASE__	( self  ,    UpperCAmelCase  ,    UpperCAmelCase  ,    UpperCAmelCase     )-> str:
     __A				 =       20
     __A				 =       model_class_name(UpperCAmelCase     )

     __A ,						__A				 =       (
         inputs_dict['''input_ids'''],
         inputs_dict['''attention_mask'''],
     )

     __A				 =       jnp.concatenate(
         [
             attention_mask,
             jnp.zeros((attention_mask.shape[0], max_length - attention_mask.shape[1])     ),
         ]  ,    axis=-1  ,    )

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

     __A				 =       model(
         input_ids[:, :-1]  ,    attention_mask=UpperCAmelCase  ,    past_key_values=UpperCAmelCase  ,    position_ids=UpperCAmelCase  ,    )
     __A				 =       jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]  ,    dtype='''i4'''     )
     __A				 =       model(
         input_ids[:, -1:]  ,    past_key_values=outputs_cache.past_key_values  ,    attention_mask=UpperCAmelCase  ,    position_ids=UpperCAmelCase  ,    )

     __A				 =       model(UpperCAmelCase  ,    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 						_lowerCAmelCase(						_a		,   unittest.TestCase		,   _a):

  """simple docstring"""







  lowerCamelCase__      					=   (FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
  lowerCamelCase__      					=   () if is_flax_available() else ()

  def 		SCREAMING_SNAKE_CASE__	( self     )-> List[Any]:
     __A				 =       FlaxOPTModelTester(self     )

  def 		SCREAMING_SNAKE_CASE__	( 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 		SCREAMING_SNAKE_CASE__	( self     )-> int:
     __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     )





  @slow
  def 		SCREAMING_SNAKE_CASE__	( self     )-> Union[str, Any]:
     for model_class_name in self.all_model_classes:
        __A				 =       model_class_name.from_pretrained('''facebook/opt-125m'''     )
        __A				 =       np.ones((1, 1)     ) * model.config.eos_token_id
        __A				 =       model(UpperCAmelCase     )
        self.assertIsNotNone(UpperCAmelCase     )







@require_sentencepiece
@require_flax
class 						_lowerCAmelCase(						unittest.TestCase):

  """simple docstring"""







  @slow
  def 		SCREAMING_SNAKE_CASE__	( self     )-> Tuple:
     __A				 =       FlaxOPTModel.from_pretrained('''facebook/opt-350m'''     )
     __A				 =       jnp.array([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]]     )
     __A				 =       model(input_ids=UpperCAmelCase     ).last_hidden_state
     __A				 =       (1, 11, 5_12)
     self.assertEqual(output.shape  ,    UpperCAmelCase     )
     __A				 =       jnp.array(
         [[-0.2867, -1.9256, -0.3062], [-1.2711, -0.1337, -0.1897], [0.4109, 0.1187, -1.3142]]     )
     self.assertTrue(jnp.allclose(output[:, :3, :3]  ,    UpperCAmelCase  ,    atol=4e-2     )     )







@require_flax
@slow
class 						_lowerCAmelCase(						unittest.TestCase):

  """simple docstring"""







  def 		SCREAMING_SNAKE_CASE__	( self     )-> List[str]:
     super().setUp()
     __A				 =       '''facebook/opt-350m'''





  def 		SCREAMING_SNAKE_CASE__	( self     )-> Dict:
     __A				 =       FlaxOPTForCausalLM.from_pretrained(self.path_model     )
     __A				 =       GPTaTokenizer.from_pretrained(self.path_model     )

     __A				 =       [
         '''Today is a beautiful day and I want to''',
         '''In the city of''',
         '''Paris is the capital of France and''',
         '''Computers and mobile phones have taken''',
     ]
     # verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
     __A				 =       tokenizer(UpperCAmelCase  ,    return_tensors='''jax'''  ,    padding=UpperCAmelCase  ,    add_special_tokens=UpperCAmelCase     )
     __A				 =       model(inputs.input_ids  ,    attention_mask=inputs.attention_mask     )[0].mean(axis=-1     )
     __A				 =       jnp.array(
         [
             [1.3851, -13.8923, -10.5229, -10.7533, -0.2309, -10.2384, -0.5365, -9.0947, -5.1670],
             [-4.7073, -10.6276, -3.9415, -21.5242, -0.2822, -0.2822, -0.2822, -0.2822, -0.2822],
             [0.6247, -3.4229, -8.9179, -1.4297, -14.1650, 1.4146, -9.0218, -0.2703, -0.2703],
             [6.4783, -1.9913, -10.7926, -2.3336, 1.5092, -0.9974, -6.8213, 1.3477, 1.3477],
         ]     )
     self.assertTrue(jnp.allclose(UpperCAmelCase  ,    UpperCAmelCase  ,    atol=4e-2     )     )

     __A				 =       jax.jit(UpperCAmelCase     )
     __A				 =       model(inputs.input_ids  ,    attention_mask=inputs.attention_mask     )[0].mean(axis=-1     )
     self.assertTrue(jnp.allclose(UpperCAmelCase  ,    UpperCAmelCase  ,    atol=4e-2     )     )







@require_flax
@slow
class 						_lowerCAmelCase(						unittest.TestCase):

  """simple docstring"""







  @property
  def 		SCREAMING_SNAKE_CASE__	( self     )-> Union[str, Any]:
     return [
         "Today is a beautiful day and I want",
         "In the city of",
         "Paris is the capital of France and",
         "Computers and mobile phones have taken",
     ]

  def 		SCREAMING_SNAKE_CASE__	( self     )-> str:
     __A				 =       '''facebook/opt-125m'''

     __A				 =       [
         '''Today is a beautiful day and I want to''',
         '''In the city of New York, the city''',
         '''Paris is the capital of France and the capital''',
         '''Computers and mobile phones have taken over the''',
     ]

     __A				 =       []

     __A				 =       FlaxOPTForCausalLM.from_pretrained(UpperCAmelCase     )
     __A				 =       GPTaTokenizer.from_pretrained(UpperCAmelCase     )

     for prompt in self.prompts:
        __A				 =       tokenizer(UpperCAmelCase  ,    return_tensors='''jax'''     ).input_ids

        __A				 =       model.generate(UpperCAmelCase  ,    max_length=10     )
        __A				 =       generated_ids[0]

        __A				 =       tokenizer.batch_decode(UpperCAmelCase  ,    skip_special_tokens=UpperCAmelCase     )
        predicted_outputs += generated_string

     self.assertListEqual(UpperCAmelCase  ,    UpperCAmelCase     )

  def 		SCREAMING_SNAKE_CASE__	( self     )-> List[Any]:
     __A				 =       '''facebook/opt-350m'''

     __A				 =       [
         '''Today is a beautiful day and I want to''',
         '''In the city of San Francisco, the city''',
         '''Paris is the capital of France and the capital''',
         '''Computers and mobile phones have taken over the''',
     ]

     __A				 =       []
     __A				 =       FlaxOPTForCausalLM.from_pretrained(UpperCAmelCase     )
     __A				 =       GPTaTokenizer.from_pretrained(UpperCAmelCase     )

     for prompt in self.prompts:
        __A				 =       tokenizer(UpperCAmelCase  ,    return_tensors='''jax'''     ).input_ids

        __A				 =       model.generate(UpperCAmelCase  ,    max_length=10     )
        __A				 =       generated_ids[0]

        __A				 =       tokenizer.batch_decode(UpperCAmelCase  ,    skip_special_tokens=UpperCAmelCase     )
        predicted_outputs += generated_string

     self.assertListEqual(UpperCAmelCase  ,    UpperCAmelCase     )

  def 		SCREAMING_SNAKE_CASE__	( self     )-> Dict:
     __A				 =       '''facebook/opt-125m'''
     __A				 =       [
         '''Today is a beautiful day and I want to thank''',
         '''In the city of Rome Canaver Canaver Canaver Canaver''',
     ]
     __A				 =       FlaxOPTForCausalLM.from_pretrained(UpperCAmelCase     )
     __A				 =       GPTaTokenizer.from_pretrained(UpperCAmelCase     )
     __A				 =       tokenizer(
         [
             '''Today is a beautiful day and I want to''',
             '''In the city of''',
         ]  ,    return_tensors='''jax'''  ,    padding=UpperCAmelCase  ,    )

     __A				 =       jax.jit(model.generate     )

     __A				 =       jit_generate(inputs['''input_ids''']  ,    attention_mask=inputs['''attention_mask''']     ).sequences

     __A				 =       tokenizer.batch_decode(UpperCAmelCase  ,    skip_special_tokens=UpperCAmelCase     )

     self.assertIsNotNone(UpperCAmelCase  ,    UpperCAmelCase     )





  def 		SCREAMING_SNAKE_CASE__	( self     )-> Any:
     __A				 =       '''facebook/opt-350m'''

     __A				 =       GPTaTokenizer.from_pretrained(UpperCAmelCase     )
     __A				 =       FlaxOPTForCausalLM.from_pretrained(UpperCAmelCase     )

     __A				 =       '''left'''

     # use different length sentences to test batching
     __A				 =       [
         '''Hello, my dog is a little''',
         '''Today, I''',
     ]

     __A				 =       tokenizer(UpperCAmelCase  ,    return_tensors='''jax'''  ,    padding=UpperCAmelCase     )
     __A				 =       inputs['''input_ids''']

     __A				 =       model.generate(input_ids=UpperCAmelCase  ,    attention_mask=inputs['''attention_mask''']  ,    trace=UpperCAmelCase     )

     __A				 =       tokenizer(sentences[0]  ,    return_tensors='''jax'''     ).input_ids
     __A				 =       model.generate(input_ids=UpperCAmelCase     )

     __A				 =       inputs_non_padded.shape[-1] - inputs['''attention_mask'''][-1].sum()
     __A				 =       tokenizer(sentences[1]  ,    return_tensors='''jax'''     ).input_ids
     __A				 =       model.generate(input_ids=UpperCAmelCase  ,    max_length=model.config.max_length - num_paddings     )

     __A				 =       tokenizer.batch_decode(outputs[0]  ,    skip_special_tokens=UpperCAmelCase     )
     __A				 =       tokenizer.decode(output_non_padded[0][0]  ,    skip_special_tokens=UpperCAmelCase     )
     __A				 =       tokenizer.decode(output_padded[0][0]  ,    skip_special_tokens=UpperCAmelCase     )

     __A				 =       [
         '''Hello, my dog is a little bit of a dork.\nI\'m a little bit''',
         '''Today, I was in the middle of a conversation with a friend about the''',
     ]
     self.assertListEqual(UpperCAmelCase  ,    UpperCAmelCase     )
     self.assertListEqual(UpperCAmelCase  ,    [non_padded_sentence, padded_sentence]     )

