





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						:       Optional[Any]        =					'''platform'''

    import jax
    import jax.numpy as jnp

    from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer







def 			_SCREAMING_SNAKE_CASE    (       a ,						a ,						a=None ,						a=None  )				->				Optional[Any]:
     if attention_mask is None:
          __A     :						List[str]          =    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					, _A					, _A=13					, _A=7					, _A=True					, _A=False					, _A=99					, _A=16					, _A=2					, _A=4					, _A=4					, _A="gelu"					, _A=0.1					, _A=0.1					, _A=20					, _A=2					, _A=1					, _A=0					, _A=16					, _A=16					, _A=0.0_2					, ):
         __A     :						int          =    parent
         __A     :						List[str]          =    batch_size
         __A     :						Dict          =    seq_length
         __A     :						Dict          =    is_training
         __A     :						List[Any]          =    use_labels
         __A     :						str          =    vocab_size
         __A     :						Union[str, Any]          =    hidden_size
         __A     :						Optional[Any]          =    num_hidden_layers
         __A     :						Tuple          =    num_attention_heads
         __A     :						Dict          =    intermediate_size
         __A     :						List[str]          =    hidden_act
         __A     :						str          =    hidden_dropout_prob
         __A     :						Union[str, Any]          =    attention_probs_dropout_prob
         __A     :						str          =    max_position_embeddings
         __A     :						Tuple          =    eos_token_id
         __A     :						Dict          =    pad_token_id
         __A     :						Tuple          =    bos_token_id
         __A     :						Any          =    embed_dim
         __A     :						Any          =    word_embed_proj_dim
         __A     :						Optional[Any]          =    initializer_range
         __A     :						List[str]          =    False



    def  UpperCAmelCase_		(      self					):
         __A     :						int          =    np.clip(ids_tensor([self.batch_size, self.seq_length - 1]					, self.vocab_size					)					, 3					, self.vocab_size					)
         __A     :						Union[str, Any]          =    np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)					, dtype=np.intaa					))					, -1					)

         __A     :						Union[str, Any]          =    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     :						int          =    prepare_opt_inputs_dict(_A					, _A					)
         return config, inputs_dict



    def  UpperCAmelCase_		(      self					):
         __A     :						Dict          =    self.prepare_config_and_inputs()
         return config, inputs_dict



    def  UpperCAmelCase_		(      self					, _A					, _A					, _A					):
         __A     :						Union[str, Any]          =    20
         __A     :						str          =    model_class_name(_A					)

         __A     :						Dict          =    inputs_dict['input_ids']
         __A     :						Optional[int]          =    inputs_dict['attention_mask']

         __A     :						int          =    model.init_cache(input_ids.shape[0]					, _A					)
         __A     :						Tuple          =    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     :						Tuple          =    model(
             input_ids[:, :-1]					, attention_mask=_A					, past_key_values=_A					, position_ids=_A					, )

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

         __A     :						List[str]          =    model(_A					)

         __A     :						Tuple          =    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  UpperCAmelCase_		(      self					, _A					, _A					, _A					):
         __A     :						Tuple          =    20
         __A     :						Optional[Any]          =    model_class_name(_A					)

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

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

         __A     :						Optional[Any]          =    model(_A					, attention_mask=_A					)

         __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}"""					)





@require_flax
class 						_A( snake_case__    ,			unittest.TestCase    ,			snake_case__       ):


    """simple docstring"""


    UpperCamelCase   :  str    =     (FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
    UpperCamelCase   :  Union[str, Any]    =     () if is_flax_available() else ()



    def  UpperCAmelCase_		(      self					):
         __A     :						Union[str, Any]          =    FlaxOPTModelTester(self					)



    def  UpperCAmelCase_		(      self					):
         __A     :						Union[str, Any]          =    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  UpperCAmelCase_		(      self					):
         __A     :						Tuple          =    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  UpperCAmelCase_		(      self					):
         for model_class_name in self.all_model_classes:
              __A     :						Tuple          =    model_class_name.from_pretrained('facebook/opt-125m'					)
              __A     :						List[str]          =    np.ones((1, 1)					) * model.config.eos_token_id
              __A     :						Any          =    model(_A					)
              self.assertIsNotNone(_A					)





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


    """simple docstring"""


    @slow
    def  UpperCAmelCase_		(      self					):
         __A     :						Dict          =    FlaxOPTModel.from_pretrained('facebook/opt-350m'					)
         __A     :						Any          =    jnp.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]					)
         __A     :						int          =    model(input_ids=_A					).last_hidden_state
         __A     :						str          =    (1, 11, 512)
         self.assertEqual(output.shape					, _A					)
         __A     :						Optional[int]          =    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 						_A( unittest.TestCase       ):


    """simple docstring"""


    def  UpperCAmelCase_		(      self					):
         super().setUp()
         __A     :						int          =    'facebook/opt-350m'





    def  UpperCAmelCase_		(      self					):
         __A     :						int          =    FlaxOPTForCausalLM.from_pretrained(self.path_model					)
         __A     :						Dict          =    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     :						Tuple          =    tokenizer(_A					, return_tensors='jax'					, padding=_A					, add_special_tokens=_A					)
         __A     :						List[Any]          =    model(inputs.input_ids					, attention_mask=inputs.attention_mask					)[0].mean(axis=-1					)
         __A     :						Optional[int]          =    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     :						List[Any]          =    jax.jit(_A					)
         __A     :						List[str]          =    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 						_A( unittest.TestCase       ):


    """simple docstring"""


    @property
    def  UpperCAmelCase_		(      self					):
         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  UpperCAmelCase_		(      self					):
         __A     :						Optional[int]          =    'facebook/opt-125m'

         __A     :						Optional[Any]          =    [
             '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     :						List[str]          =    []

         __A     :						List[Any]          =    FlaxOPTForCausalLM.from_pretrained(_A					)
         __A     :						Optional[int]          =    GPTaTokenizer.from_pretrained(_A					)

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

              __A     :						Any          =    model.generate(_A					, max_length=10					)
              __A     :						Any          =    generated_ids[0]

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

         self.assertListEqual(_A					, _A					)



    def  UpperCAmelCase_		(      self					):
         __A     :						Dict          =    'facebook/opt-350m'

         __A     :						List[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     :						Dict          =    []
         __A     :						int          =    FlaxOPTForCausalLM.from_pretrained(_A					)
         __A     :						str          =    GPTaTokenizer.from_pretrained(_A					)

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

              __A     :						int          =    model.generate(_A					, max_length=10					)
              __A     :						List[Any]          =    generated_ids[0]

              __A     :						List[Any]          =    tokenizer.batch_decode(_A					, skip_special_tokens=_A					)
              predicted_outputs += generated_string

         self.assertListEqual(_A					, _A					)



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

         __A     :						str          =    jax.jit(model.generate					)

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

         __A     :						Optional[Any]          =    tokenizer.batch_decode(_A					, skip_special_tokens=_A					)

         self.assertIsNotNone(_A					, _A					)





    def  UpperCAmelCase_		(      self					):
         __A     :						Tuple          =    'facebook/opt-350m'

         __A     :						Optional[Any]          =    GPTaTokenizer.from_pretrained(_A					)
         __A     :						Dict          =    FlaxOPTForCausalLM.from_pretrained(_A					)

         __A     :						int          =    'left'

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

         __A     :						List[str]          =    tokenizer(_A					, return_tensors='jax'					, padding=_A					)
         __A     :						Tuple          =    inputs['input_ids']

         __A     :						Tuple          =    model.generate(input_ids=_A					, attention_mask=inputs['attention_mask']					, trace=_A					)

         __A     :						Any          =    tokenizer(sentences[0]					, return_tensors='jax'					).input_ids
         __A     :						List[str]          =    model.generate(input_ids=_A					)

         __A     :						str          =    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=_A					, max_length=model.config.max_length - num_paddings					)

         __A     :						Tuple          =    tokenizer.batch_decode(outputs[0]					, skip_special_tokens=_A					)
         __A     :						List[Any]          =    tokenizer.decode(output_non_padded[0][0]					, skip_special_tokens=_A					)
         __A     :						Tuple          =    tokenizer.decode(output_padded[0][0]					, skip_special_tokens=_A					)

         __A     :						int          =    [
             '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 __future__ import annotations

import math
import random







def 			_SCREAMING_SNAKE_CASE    (       a ,						a ,						a  )				->				list[int]:
					__A     :						int          =    d * e - 1
					__A     :						List[str]          =    0
					__A     :						Optional[Any]          =    0
					while p == 0:
										__A     :						List[Any]          =    random.randint(2 ,						n - 1  )
										__A     :						Optional[int]          =    k
										while True:
															if t % 2 == 0:
																				__A     :						Tuple          =    t // 2
																				__A     :						List[str]          =    (g**t) % n
																				__A     :						List[Any]          =    math.gcd(x - 1 ,						a  )
																				if x > 1 and y > 1:
																									__A     :						List[str]          =    y
																									__A     :						str          =    n // y
																									break  # find the correct factors
															else:
																				break  # t is not divisible by 2, break and choose another g
					return sorted([p, q]  )


if __name__ == "__main__":
				import doctest

				doctest.testmod()




