



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
     A_		:  str										=  '''platform'''

     import jax
     import jax.numpy as jnp

     from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer


def 						UpperCAmelCase__   (   UpperCAmelCase__			:Optional[Any]			,       UpperCAmelCase__			:Tuple			,       UpperCAmelCase__			:str=None			,       UpperCAmelCase__			:List[Any]=None       ):






 '''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 		_lowercase		:

   def __init__(   self      :							List[str]				, __lowerCAmelCase      :							Optional[int]				, __lowerCAmelCase      :							Optional[Any]=13				, __lowerCAmelCase      :							Optional[Any]=7				, __lowerCAmelCase      :							List[Any]=True				, __lowerCAmelCase      :							Any=False				, __lowerCAmelCase      :							Dict=99				, __lowerCAmelCase      :							Any=16				, __lowerCAmelCase      :							Optional[int]=2				, __lowerCAmelCase      :							Tuple=4				, __lowerCAmelCase      :							Optional[int]=4				, __lowerCAmelCase      :							int="gelu"				, __lowerCAmelCase      :							Tuple=0.1				, __lowerCAmelCase      :							Optional[Any]=0.1				, __lowerCAmelCase      :							Optional[Any]=20				, __lowerCAmelCase      :							int=2				, __lowerCAmelCase      :							str=1				, __lowerCAmelCase      :							Any=0				, __lowerCAmelCase      :							Tuple=16				, __lowerCAmelCase      :							Optional[int]=16				, __lowerCAmelCase      :							Optional[Any]=0.0_2				, )							->					List[Any]:


    """simple docstring"""






    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 		A				(   self      :							str				)							->					Dict:


    """simple docstring"""






    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=_A				, word_embed_proj_dim=self.word_embed_proj_dim				, initializer_range=self.initializer_range				, use_cache=_A				, )
    a							=		prepare_opt_inputs_dict(_A				, _A				)
    return config, inputs_dict

   def 		A				(   self      :							List[Any]				)							->					Union[str, Any]:


    """simple docstring"""






    a							=		self.prepare_config_and_inputs()
    return config, inputs_dict

   def 		A				(   self      :							Union[str, Any]				, __lowerCAmelCase      :							Tuple				, __lowerCAmelCase      :							Optional[Any]				, __lowerCAmelCase      :							Dict				)							->					Any:


    """simple docstring"""






    a							=		20
    a							=		model_class_name(_A				)

    a							=		inputs_dict['input_ids']
    a							=		inputs_dict['attention_mask']

    a							=		model.init_cache(input_ids.shape[0]				, _A				)
    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=_A				, past_key_values=_A				, position_ids=_A				, )

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

    a							=		model(_A				)

    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      :							List[Any]				, __lowerCAmelCase      :							int				, __lowerCAmelCase      :							List[str]				, __lowerCAmelCase      :							Tuple				)							->					List[Any]:


    """simple docstring"""






    a							=		20
    a							=		model_class_name(_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]				, _A				)
    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=_A				, past_key_values=_A				, position_ids=_A				, )
    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=_A				, position_ids=_A				, )

    a							=		model(_A				, attention_mask=_A				)

    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 		_lowercase		(							snake_case_, unittest.TestCase, snake_case_						):
   _UpperCAmelCase  						=      (FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
   _UpperCAmelCase  						=      () if is_flax_available() else ()

   def 		A				(   self      :							Any				)							->					Union[str, Any]:


    """simple docstring"""






    a							=		FlaxOPTModelTester(self				)

   def 		A				(   self      :							Optional[int]				)							->					List[Any]:


    """simple docstring"""






    a							=		self.model_tester.prepare_config_and_inputs()
    for model_class in self.all_model_classes:
     self.model_tester.check_use_cache_forward(_A				, _A				, _A				)

   def 		A				(   self      :							Optional[Any]				)							->					Optional[Any]:


    """simple docstring"""






    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(_A				, _A				, _A				)




   @slow
   def 		A				(   self      :							int				)							->					Union[str, Any]:


    """simple docstring"""






    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(_A				)
     self.assertIsNotNone(_A				)



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

   @slow
   def 		A				(   self      :							Tuple				)							->					Optional[int]:


    """simple docstring"""






    a							=		FlaxOPTModel.from_pretrained("facebook/opt-350m"				)
    a							=		jnp.array([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]				)
    a							=		model(input_ids=_A				).last_hidden_state
    a							=		(1, 11, 512)
    self.assertEqual(output.shape				, _A				)
    a							=		jnp.array(
        [[-0.2_8_6_7, -1.9_2_5_6, -0.3_0_6_2], [-1.2_7_1_1, -0.1_3_3_7, -0.1_8_9_7], [0.4_1_0_9, 0.1_1_8_7, -1.3_1_4_2]]				)
    self.assertTrue(jnp.allclose(output[:, :3, :3]				, _A				, atol=4E-2				)				)



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

   def 		A				(   self      :							List[str]				)							->					str:


    """simple docstring"""






    super().setUp()
    a							=		'facebook/opt-350m'




   def 		A				(   self      :							Optional[Any]				)							->					Tuple:


    """simple docstring"""






    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(_A				, return_tensors="jax"				, padding=_A				, add_special_tokens=_A				)
    a							=		model(inputs.input_ids				, attention_mask=inputs.attention_mask				)[0].mean(axis=-1				)
    a							=		jnp.array(
        [
            [1.3_8_5_1, -13.8923, -10.5229, -10.7533, -0.2_3_0_9, -10.2384, -0.5_3_6_5, -9.0_9_4_7, -5.1_6_7_0],
            [-4.7_0_7_3, -10.6276, -3.9_4_1_5, -21.5242, -0.2_8_2_2, -0.2_8_2_2, -0.2_8_2_2, -0.2_8_2_2, -0.2_8_2_2],
            [0.6_2_4_7, -3.4_2_2_9, -8.9_1_7_9, -1.4_2_9_7, -14.1650, 1.4_1_4_6, -9.0_2_1_8, -0.2_7_0_3, -0.2_7_0_3],
            [6.4_7_8_3, -1.9_9_1_3, -10.7926, -2.3_3_3_6, 1.5_0_9_2, -0.9_9_7_4, -6.8_2_1_3, 1.3_4_7_7, 1.3_4_7_7],
        ]				)
    self.assertTrue(jnp.allclose(_A				, _A				, atol=4E-2				)				)

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



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

   @property
   def 		A				(   self      :							List[str]				)							->					int:


    """simple docstring"""






    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 		A				(   self      :							str				)							->					Dict:


    """simple docstring"""






    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(_A				)
    a							=		GPTaTokenizer.from_pretrained(_A				)

    for prompt in self.prompts:
     a							=		tokenizer(_A				, return_tensors="jax"				).input_ids

     a							=		model.generate(_A				, max_length=10				)
     a							=		generated_ids[0]

     a							=		tokenizer.batch_decode(_A				, skip_special_tokens=_A				)
     predicted_outputs += generated_string

    self.assertListEqual(_A				, _A				)

   def 		A				(   self      :							Union[str, Any]				)							->					int:


    """simple docstring"""






    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(_A				)
    a							=		GPTaTokenizer.from_pretrained(_A				)

    for prompt in self.prompts:
     a							=		tokenizer(_A				, return_tensors="jax"				).input_ids

     a							=		model.generate(_A				, max_length=10				)
     a							=		generated_ids[0]

     a							=		tokenizer.batch_decode(_A				, skip_special_tokens=_A				)
     predicted_outputs += generated_string

    self.assertListEqual(_A				, _A				)

   def 		A				(   self      :							int				)							->					Optional[int]:


    """simple docstring"""






    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(_A				)
    a							=		GPTaTokenizer.from_pretrained(_A				)
    a							=		tokenizer(
        [
            "Today is a beautiful day and I want to",
            "In the city of",
        ]				, return_tensors="jax"				, padding=_A				, )

    a							=		jax.jit(model.generate				)

    a							=		jit_generate(inputs["input_ids"]				, attention_mask=inputs["attention_mask"]				).sequences

    a							=		tokenizer.batch_decode(_A				, skip_special_tokens=_A				)

    self.assertIsNotNone(_A				, _A				)




   def 		A				(   self      :							int				)							->					Optional[int]:


    """simple docstring"""






    a							=		'facebook/opt-350m'

    a							=		GPTaTokenizer.from_pretrained(_A				)
    a							=		FlaxOPTForCausalLM.from_pretrained(_A				)

    a							=		'left'

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

    a							=		tokenizer(_A				, return_tensors="jax"				, padding=_A				)
    a							=		inputs['input_ids']

    a							=		model.generate(input_ids=_A				, attention_mask=inputs["attention_mask"]				, trace=_A				)

    a							=		tokenizer(sentences[0]				, return_tensors="jax"				).input_ids
    a							=		model.generate(input_ids=_A				)

    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=_A				, max_length=model.config.max_length - num_paddings				)

    a							=		tokenizer.batch_decode(outputs[0]				, skip_special_tokens=_A				)
    a							=		tokenizer.decode(output_non_padded[0][0]				, skip_special_tokens=_A				)
    a							=		tokenizer.decode(output_padded[0][0]				, skip_special_tokens=_A				)

    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(_A				, _A				)
    self.assertListEqual(_A				, [non_padded_sentence, padded_sentence]				)








from ...configuration_utils import PretrainedConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE   						=		logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE   						=		{
    """uw-madison/nystromformer-512""": """https://huggingface.co/uw-madison/nystromformer-512/resolve/main/config.json""",
    # See all Nystromformer models at https://huggingface.co/models?filter=nystromformer
}
class SCREAMING_SNAKE_CASE_    (       snake_case_       ):
						__magic_name__:      Optional[Any]			   =							"nystromformer"






						def __init__(   self   :   List[str]		, _A   :   Optional[Any]=30000		, _A   :   str=768		, _A   :   Optional[int]=12		, _A   :   Dict=12		, _A   :   Any=3072		, _A   :   str="gelu_new"		, _A   :   Optional[Any]=0.1		, _A   :   int=0.1		, _A   :   Dict=510		, _A   :   List[Any]=2		, _A   :   Optional[Any]=64		, _A   :   Union[str, Any]=64		, _A   :   List[Any]=65		, _A   :   Dict=False		, _A   :   str=0.0_2		, _A   :   Dict=1E-5		, _A   :   List[Any]=1		, _A   :   Optional[int]=0		, _A   :   List[Any]=2		, **_A   :   int		, )				->     int:







							"""simple docstring"""


							snake_case_		:      Union[str, Any]           =						vocab_size
							snake_case_		:      str           =						max_position_embeddings
							snake_case_		:      Tuple           =						hidden_size
							snake_case_		:      Union[str, Any]           =						num_hidden_layers
							snake_case_		:      str           =						num_attention_heads
							snake_case_		:      Optional[Any]           =						intermediate_size
							snake_case_		:      str           =						hidden_act
							snake_case_		:      str           =						hidden_dropout_prob
							snake_case_		:      Optional[Any]           =						attention_probs_dropout_prob
							snake_case_		:      Union[str, Any]           =						initializer_range
							snake_case_		:      Union[str, Any]           =						type_vocab_size
							snake_case_		:      Dict           =						segment_means_seq_len
							snake_case_		:      Tuple           =						num_landmarks
							snake_case_		:      Any           =						conv_kernel_size
							snake_case_		:      List[str]           =						inv_coeff_init_option
							snake_case_		:      str           =						layer_norm_eps
							super().__init__(pad_token_id=_A		, bos_token_id=_A		, eos_token_id=_A		, **_A						)

