





import os
import sys
from pathlib import Path

import pytest

from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node

from .utils import execute_subprocess_async, get_torch_dist_unique_port, require_torch
def 				__UpperCamelCase  (		)  ->						Any:


   '''simple docstring'''







   __A				 =       Dataset.from_dict({'''i''': range(1_7     )}     )
   __A				 =       len(snake_case     )
   __A				 =       3
   __A				 =       [
       split_dataset_by_node(snake_case    ,	rank=snake_case    ,	world_size=snake_case     ) for rank in range(snake_case     )
   ]
   assert sum(len(snake_case     ) for ds in datasets_per_rank     ) == full_size
   assert len({tuple(x.values()     ) for ds in datasets_per_rank for x in ds}     ) == full_size
def 				__UpperCamelCase  (		)  ->						Union[str, Any]:


   '''simple docstring'''







   def gen():
      return ({"i": i} for i in range(1_7     ))

   __A				 =       3
   __A				 =       IterableDataset.from_generator(snake_case     )
   __A				 =       len(list(snake_case     )     )
   __A				 =       [
       split_dataset_by_node(snake_case    ,	rank=snake_case    ,	world_size=snake_case     ) for rank in range(snake_case     )
   ]
   assert sum(len(list(snake_case     )     ) for ds in datasets_per_rank     ) == full_size
   assert len({tuple(x.values()     ) for ds in datasets_per_rank for x in ds}     ) == full_size
@pytest.mark.parametrize('''shards_per_node'''    ,	[1, 2, 3]     )
def 				__UpperCamelCase  (		snake_case     )  ->						int:


   '''simple docstring'''







   def gen(snake_case     ):
      for shard in shards:
         yield from ({"i": i, "shard": shard} for i in range(1_7     ))

   __A				 =       3
   __A				 =       shards_per_node * world_size
   __A				 =       {'''shards''': [F"shard_{shard_idx}.txt" for shard_idx in range(snake_case     )]}
   __A				 =       IterableDataset.from_generator(snake_case    ,	gen_kwargs=snake_case     )
   __A				 =       len(list(snake_case     )     )
   assert full_ds.n_shards == world_size * shards_per_node
   __A				 =       [
       split_dataset_by_node(snake_case    ,	rank=snake_case    ,	world_size=snake_case     ) for rank in range(snake_case     )
   ]
   assert [ds.n_shards for ds in datasets_per_rank] == [shards_per_node] * world_size
   assert sum(len(list(snake_case     )     ) for ds in datasets_per_rank     ) == full_size
   assert len({tuple(x.values()     ) for ds in datasets_per_rank for x in ds}     ) == full_size
def 				__UpperCamelCase  (		)  ->						Optional[int]:


   '''simple docstring'''







   def gen():
      return ({"i": i} for i in range(1_7     ))

   __A				 =       2
   __A				 =       IterableDataset.from_generator(snake_case     )
   __A				 =       len(list(snake_case     )     )

   __A				 =       split_dataset_by_node(snake_case    ,	rank=0    ,	world_size=snake_case     ).shuffle(seed=4_2     )
   assert len(list(snake_case     )     ) == 1 + full_size // world_size
   with pytest.raises(snake_case     ):
      split_dataset_by_node(snake_case    ,	rank=0    ,	world_size=snake_case     ).shuffle()

   __A				 =       split_dataset_by_node(full_ds.shuffle(seed=4_2     )    ,	rank=0    ,	world_size=snake_case     )
   assert len(list(snake_case     )     ) == 1 + full_size // world_size
   with pytest.raises(snake_case     ):
      split_dataset_by_node(full_ds.shuffle()    ,	rank=0    ,	world_size=snake_case     )
@pytest.mark.parametrize('''streaming'''    ,	[False, True]     )
@require_torch
@pytest.mark.skipif(os.name == '''nt'''    ,	reason='''execute_subprocess_async doesn\'t support windows'''     )
@pytest.mark.integration
def 				__UpperCamelCase  (		snake_case     )  ->						Union[str, Any]:


   '''simple docstring'''







   __A				 =       2
   __A				 =       get_torch_dist_unique_port()
   __A				 =       Path(snake_case     ).resolve().parent / '''distributed_scripts''' / '''launch_torch_distributed.py'''
   __A				 =       F"\n        -m torch.distributed.launch\n        --nproc_per_node={nproc_per_node}\n        --master_port={master_port}\n        {test_script}\n    ".split()
   __A				 =       F"\n        --streaming={streaming}\n    ".split()
   __A				 =       [sys.executable] + distributed_args + args
   execute_subprocess_async(snake_case    ,	env=os.environ.copy()     )
@pytest.mark.parametrize(
    '''nproc_per_node, num_workers'''    ,	[
        (2, 2),  # each node has 2 shards and each worker has 1 shards
        (3, 2),  # each node uses all the shards but skips examples, and each worker has 2 shards
    ]    ,	)
@require_torch
@pytest.mark.skipif(os.name == '''nt'''    ,	reason='''execute_subprocess_async doesn\'t support windows'''     )
@pytest.mark.integration
def 				__UpperCamelCase  (		snake_case    ,	snake_case     )  ->						str:


   '''simple docstring'''







   __A				 =       True
   __A				 =       get_torch_dist_unique_port()
   __A				 =       Path(snake_case     ).resolve().parent / '''distributed_scripts''' / '''launch_torch_distributed.py'''
   __A				 =       F"\n        -m torch.distributed.launch\n        --nproc_per_node={nproc_per_node}\n        --master_port={master_port}\n        {test_script}\n    ".split()
   __A				 =       F"\n        --streaming={streaming}\n        --num_workers={num_workers}\n    ".split()
   __A				 =       [sys.executable] + distributed_args + args
   execute_subprocess_async(snake_case    ,	env=os.environ.copy()     )








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]     )

