


"""simple docstring"""






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
 SCREAMING_SNAKE_CASE__				:  Any						= "platform"

 import jax
 import jax.numpy as jnp

 from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer




def        A_					(					UpperCAmelCase__       ,   UpperCAmelCase__       ,   UpperCAmelCase__=None       ,   UpperCAmelCase__=None				)     ->     Optional[Any]:
   if attention_mask is None:
      a   :	Optional[Any]						=    np.where(input_ids != config.pad_token_id       ,   1       ,   0				)
   return {
       "input_ids": input_ids,
       "attention_mask": attention_mask,
   }


@require_flax
class 	A_       :





  """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  ,					)				->					Optional[int]:
     a   :	Dict						=    parent
     a   :	Optional[int]						=    batch_size
     a   :	Union[str, Any]						=    seq_length
     a   :	Tuple						=    is_training
     a   :	Tuple						=    use_labels
     a   :	Tuple						=    vocab_size
     a   :	Any						=    hidden_size
     a   :	Dict						=    num_hidden_layers
     a   :	Dict						=    num_attention_heads
     a   :	Optional[int]						=    intermediate_size
     a   :	Any						=    hidden_act
     a   :	Optional[int]						=    hidden_dropout_prob
     a   :	Any						=    attention_probs_dropout_prob
     a   :	Tuple						=    max_position_embeddings
     a   :	Tuple						=    eos_token_id
     a   :	List[Any]						=    pad_token_id
     a   :	str						=    bos_token_id
     a   :	str						=    embed_dim
     a   :	Optional[Any]						=    word_embed_proj_dim
     a   :	Dict						=    initializer_range
     a   :	List[str]						=    False
  def 							lowercase_     (						self						)				->					Dict:
     a   :	Dict						=    np.clip(ids_tensor([self.batch_size, self.seq_length - 1]  ,					self.vocab_size						)  ,					3  ,					self.vocab_size						)
     a   :	Any						=    np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)  ,					dtype=np.intaa						))  ,					-1						)

     a   :	int						=    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   :	Any						=    prepare_opt_inputs_dict(__UpperCAmelCase  ,					__UpperCAmelCase						)
     return config, inputs_dict
  def 							lowercase_     (						self						)				->					Optional[Any]:
     a	, a   :	Optional[Any]						=    self.prepare_config_and_inputs()
     return config, inputs_dict
  def 							lowercase_     (						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase						)				->					List[str]:
     a   :	List[Any]						=    20
     a   :	str						=    model_class_name(__UpperCAmelCase						)

     a   :	str						=    inputs_dict['input_ids']
     a   :	Union[str, Any]						=    inputs_dict['attention_mask']

     a   :	List[Any]						=    model.init_cache(input_ids.shape[0]  ,					__UpperCAmelCase						)
     a   :	Optional[int]						=    jnp.ones((input_ids.shape[0], max_length)  ,					dtype='i4'						)

     a   :	Optional[Any]						=    jnp.broadcast_to(
         jnp.arange(input_ids.shape[-1] - 1						)[None, :]  ,					(input_ids.shape[0], input_ids.shape[-1] - 1)  ,					)
     a   :	int						=    model(
         input_ids[:, :-1]  ,					attention_mask=__UpperCAmelCase  ,					past_key_values=__UpperCAmelCase  ,					position_ids=__UpperCAmelCase  ,					)

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

     a   :	int						=    model(__UpperCAmelCase						)

     a   :	str						=    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 							lowercase_     (						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase						)				->					Tuple:
     a   :	Tuple						=    20
     a   :	Optional[Any]						=    model_class_name(__UpperCAmelCase						)

     a	, a   :	List[str]						=    (
         inputs_dict['input_ids'],
         inputs_dict['attention_mask'],
     )

     a   :	List[Any]						=    jnp.concatenate(
         [
             attention_mask,
             jnp.zeros((attention_mask.shape[0], max_length - attention_mask.shape[1])						),
         ]  ,					axis=-1  ,					)

     a   :	List[str]						=    model.init_cache(input_ids.shape[0]  ,					__UpperCAmelCase						)
     a   :	Dict						=    jnp.broadcast_to(
         jnp.arange(input_ids.shape[-1] - 1						)[None, :]  ,					(input_ids.shape[0], input_ids.shape[-1] - 1)  ,					)

     a   :	Optional[Any]						=    model(
         input_ids[:, :-1]  ,					attention_mask=__UpperCAmelCase  ,					past_key_values=__UpperCAmelCase  ,					position_ids=__UpperCAmelCase  ,					)
     a   :	Optional[int]						=    jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]  ,					dtype='i4'						)
     a   :	Tuple						=    model(
         input_ids[:, -1:]  ,					past_key_values=outputs_cache.past_key_values  ,					attention_mask=__UpperCAmelCase  ,					position_ids=__UpperCAmelCase  ,					)

     a   :	Dict						=    model(__UpperCAmelCase  ,					attention_mask=__UpperCAmelCase						)

     a   :	Dict						=    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"""

  lowercase		:	Dict   =						(FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
  lowercase		:	List[str]   =						() if is_flax_available() else ()
  def 							lowercase_     (						self						)				->					Tuple:
     a   :	Optional[int]						=    FlaxOPTModelTester(self						)
  def 							lowercase_     (						self						)				->					Union[str, Any]:
     a	, a   :	Dict						=    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 							lowercase_     (						self						)				->					Any:
     a	, a   :	Optional[Any]						=    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 							lowercase_     (						self						)				->					Optional[Any]:
     for model_class_name in self.all_model_classes:
        a   :	Optional[Any]						=    model_class_name.from_pretrained('facebook/opt-125m'						)
        a   :	Any						=    np.ones((1, 1)						) * model.config.eos_token_id
        a   :	Optional[int]						=    model(__UpperCAmelCase						)
        self.assertIsNotNone(__UpperCAmelCase						)



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





  """simple docstring"""

  @slow
  def 							lowercase_     (						self						)				->					Tuple:
     a   :	int						=    FlaxOPTModel.from_pretrained('facebook/opt-350m'						)
     a   :	Optional[Any]						=    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   :	str						=    model(input_ids=__UpperCAmelCase						).last_hidden_state
     a   :	Dict						=    (1, 11, 5_12)
     self.assertEqual(output.shape  ,					__UpperCAmelCase						)
     a   :	Union[str, Any]						=    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 	A_       (     unittest.TestCase   ):





  """simple docstring"""

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






  def 							lowercase_     (						self						)				->					List[str]:
     a   :	Union[str, Any]						=    FlaxOPTForCausalLM.from_pretrained(self.path_model						)
     a   :	Any						=    GPTaTokenizer.from_pretrained(self.path_model						)

     a   :	List[str]						=    [
         '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   :	Optional[int]						=    tokenizer(__UpperCAmelCase  ,					return_tensors='jax'  ,					padding=__UpperCAmelCase  ,					add_special_tokens=__UpperCAmelCase						)
     a   :	str						=    model(inputs.input_ids  ,					attention_mask=inputs.attention_mask						)[0].mean(axis=-1						)
     a   :	Dict						=    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   :	Dict						=    jax.jit(__UpperCAmelCase						)
     a   :	Any						=    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 	A_       (     unittest.TestCase   ):





  """simple docstring"""

  @property
  def 							lowercase_     (						self						)				->					Dict:
     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 							lowercase_     (						self						)				->					Optional[Any]:
     a   :	int						=    'facebook/opt-125m'

     a   :	str						=    [
         '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   :	Tuple						=    []

     a   :	int						=    FlaxOPTForCausalLM.from_pretrained(__UpperCAmelCase						)
     a   :	Tuple						=    GPTaTokenizer.from_pretrained(__UpperCAmelCase						)

     for prompt in self.prompts:
        a   :	Union[str, Any]						=    tokenizer(__UpperCAmelCase  ,					return_tensors='jax'						).input_ids

        a   :	str						=    model.generate(__UpperCAmelCase  ,					max_length=10						)
        a   :	str						=    generated_ids[0]

        a   :	Any						=    tokenizer.batch_decode(__UpperCAmelCase  ,					skip_special_tokens=__UpperCAmelCase						)
        predicted_outputs += generated_string

     self.assertListEqual(__UpperCAmelCase  ,					__UpperCAmelCase						)
  def 							lowercase_     (						self						)				->					Union[str, Any]:
     a   :	Optional[int]						=    'facebook/opt-350m'

     a   :	str						=    [
         '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   :	List[Any]						=    []
     a   :	int						=    FlaxOPTForCausalLM.from_pretrained(__UpperCAmelCase						)
     a   :	Any						=    GPTaTokenizer.from_pretrained(__UpperCAmelCase						)

     for prompt in self.prompts:
        a   :	Dict						=    tokenizer(__UpperCAmelCase  ,					return_tensors='jax'						).input_ids

        a   :	List[str]						=    model.generate(__UpperCAmelCase  ,					max_length=10						)
        a   :	Optional[int]						=    generated_ids[0]

        a   :	Optional[Any]						=    tokenizer.batch_decode(__UpperCAmelCase  ,					skip_special_tokens=__UpperCAmelCase						)
        predicted_outputs += generated_string

     self.assertListEqual(__UpperCAmelCase  ,					__UpperCAmelCase						)
  def 							lowercase_     (						self						)				->					Union[str, Any]:
     a   :	List[str]						=    'facebook/opt-125m'
     a   :	Optional[Any]						=    [
         'Today is a beautiful day and I want to thank',
         'In the city of Rome Canaver Canaver Canaver Canaver',
     ]
     a   :	str						=    FlaxOPTForCausalLM.from_pretrained(__UpperCAmelCase						)
     a   :	Tuple						=    GPTaTokenizer.from_pretrained(__UpperCAmelCase						)
     a   :	List[str]						=    tokenizer(
         [
             'Today is a beautiful day and I want to',
             'In the city of',
         ]  ,					return_tensors='jax'  ,					padding=__UpperCAmelCase  ,					)

     a   :	Dict						=    jax.jit(model.generate						)

     a   :	Union[str, Any]						=    jit_generate(inputs['input_ids']  ,					attention_mask=inputs['attention_mask']						).sequences

     a   :	List[str]						=    tokenizer.batch_decode(__UpperCAmelCase  ,					skip_special_tokens=__UpperCAmelCase						)

     self.assertIsNotNone(__UpperCAmelCase  ,					__UpperCAmelCase						)






  def 							lowercase_     (						self						)				->					Union[str, Any]:
     a   :	List[Any]						=    'facebook/opt-350m'

     a   :	Tuple						=    GPTaTokenizer.from_pretrained(__UpperCAmelCase						)
     a   :	List[Any]						=    FlaxOPTForCausalLM.from_pretrained(__UpperCAmelCase						)

     a   :	Union[str, Any]						=    'left'

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

     a   :	Dict						=    tokenizer(__UpperCAmelCase  ,					return_tensors='jax'  ,					padding=__UpperCAmelCase						)
     a   :	List[Any]						=    inputs['input_ids']

     a   :	List[str]						=    model.generate(input_ids=__UpperCAmelCase  ,					attention_mask=inputs['attention_mask']  ,					trace=__UpperCAmelCase						)

     a   :	str						=    tokenizer(sentences[0]  ,					return_tensors='jax'						).input_ids
     a   :	Dict						=    model.generate(input_ids=__UpperCAmelCase						)

     a   :	Optional[Any]						=    inputs_non_padded.shape[-1] - inputs['attention_mask'][-1].sum()
     a   :	Union[str, Any]						=    tokenizer(sentences[1]  ,					return_tensors='jax'						).input_ids
     a   :	Optional[Any]						=    model.generate(input_ids=__UpperCAmelCase  ,					max_length=model.config.max_length - num_paddings						)

     a   :	str						=    tokenizer.batch_decode(outputs[0]  ,					skip_special_tokens=__UpperCAmelCase						)
     a   :	int						=    tokenizer.decode(output_non_padded[0][0]  ,					skip_special_tokens=__UpperCAmelCase						)
     a   :	Optional[int]						=    tokenizer.decode(output_padded[0][0]  ,					skip_special_tokens=__UpperCAmelCase						)

     a   :	Optional[Any]						=    [
         '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]						)









"""simple docstring"""






from transformers.models.cpm.tokenization_cpm import CpmTokenizer
from transformers.testing_utils import custom_tokenizers

from ..xlnet.test_modeling_xlnet import XLNetModelTest


@custom_tokenizers
class 	A_       (     _UpperCAmelCase   ):





  """simple docstring"""

  def 							lowercase_     (						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase						)				->					int:
     return True






  def 							lowercase_     (						self						)				->					int:
     a   :	Optional[int]						=    CpmTokenizer.from_pretrained('TsinghuaAI/CPM-Generate'						)
     a   :	Any						=    'Hugging Face大法好，谁用谁知道。'
     a   :	Tuple						=    'Hugging Face大法好,谁用谁知道。<unk>'
     a   :	Optional[int]						=    '▁Hu gg ing ▁ ▂ ▁F ace ▁大法 ▁好 ▁ , ▁谁 ▁用 ▁谁 ▁知 道 ▁ 。'.split()

     a   :	Optional[int]						=    tokenizer.tokenize(__UpperCAmelCase						)
     self.assertListEqual(__UpperCAmelCase  ,					__UpperCAmelCase						)

     a   :	Optional[Any]						=    tokens + [tokenizer.unk_token]

     a   :	str						=    [1_37_89, 1_32_83, 14_21, 8, 10, 11_64, 1_36_08, 1_65_28, 63, 8, 9, 4_40, 1_08, 4_40, 1_21, 90, 8, 12, 0]
     self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCAmelCase						)  ,					__UpperCAmelCase						)

     a   :	List[Any]						=    tokenizer.decode(__UpperCAmelCase						)
     self.assertEqual(__UpperCAmelCase  ,					__UpperCAmelCase						)





