





'''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
 lowerCAmelCase      :  Any      							='''platform'''

 import jax
 import jax.numpy as jnp

 from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer
def 							UpperCAmelCase_						(		__lowerCamelCase      :      Any			,__lowerCamelCase      :      str			,__lowerCamelCase      :      Any=None			,__lowerCamelCase      :      Any=None				):
   if attention_mask is None:
      lowercase_    :Tuple					    =				np.where(input_ids != config.pad_token_id			,1			,0				)
   return {
       "input_ids": input_ids,
       "attention_mask": attention_mask,
   }
@require_flax
class    a_     :





     def __init__(				self	:      Dict			,   lowercase	:      int			,   lowercase	:      Union[str, Any]=13			,   lowercase	:      Tuple=7			,   lowercase	:      List[str]=True			,   lowercase	:      Any=False			,   lowercase	:      Optional[Any]=99			,   lowercase	:      Tuple=16			,   lowercase	:      Dict=2			,   lowercase	:      List[str]=4			,   lowercase	:      str=4			,   lowercase	:      List[Any]="gelu"			,   lowercase	:      List[Any]=0.1			,   lowercase	:      str=0.1			,   lowercase	:      Union[str, Any]=20			,   lowercase	:      Tuple=2			,   lowercase	:      Tuple=1			,   lowercase	:      Optional[Any]=0			,   lowercase	:      Optional[Any]=16			,   lowercase	:      Any=16			,   lowercase	:      Tuple=0.02			,   ):





        """simple docstring"""




        lowercase_    :List[Any]					    =				parent
        lowercase_    :int					    =				batch_size
        lowercase_    :Dict					    =				seq_length
        lowercase_    :Optional[int]					    =				is_training
        lowercase_    :int					    =				use_labels
        lowercase_    :Union[str, Any]					    =				vocab_size
        lowercase_    :Any					    =				hidden_size
        lowercase_    :int					    =				num_hidden_layers
        lowercase_    :Optional[Any]					    =				num_attention_heads
        lowercase_    :int					    =				intermediate_size
        lowercase_    :Optional[Any]					    =				hidden_act
        lowercase_    :Union[str, Any]					    =				hidden_dropout_prob
        lowercase_    :List[str]					    =				attention_probs_dropout_prob
        lowercase_    :str					    =				max_position_embeddings
        lowercase_    :Optional[int]					    =				eos_token_id
        lowercase_    :Union[str, Any]					    =				pad_token_id
        lowercase_    :Union[str, Any]					    =				bos_token_id
        lowercase_    :Optional[Any]					    =				embed_dim
        lowercase_    :Optional[Any]					    =				word_embed_proj_dim
        lowercase_    :str					    =				initializer_range
        lowercase_    :Dict					    =				False





     def 							lowercase__   (				self	:      Optional[int]		):





        """simple docstring"""




        lowercase_    :Optional[Any]					    =				np.clip(ids_tensor([self.batch_size, self.seq_length - 1]			,   self.vocab_size		)			,   3			,   self.vocab_size		)
        lowercase_    :Dict					    =				np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)			,   dtype=np.intaa		))			,   -1		)

        lowercase_    :Optional[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=lowercase			,   word_embed_proj_dim=self.word_embed_proj_dim			,   initializer_range=self.initializer_range			,   use_cache=lowercase			,   )
        lowercase_    :Optional[Any]					    =				prepare_opt_inputs_dict(lowercase			,   lowercase		)
        return config, inputs_dict





     def 							lowercase__   (				self	:      Tuple		):





        """simple docstring"""




        lowercase_    ,							lowercase_    :Any					    =				self.prepare_config_and_inputs()
        return config, inputs_dict





     def 							lowercase__   (				self	:      Any			,   lowercase	:      Tuple			,   lowercase	:      Optional[int]			,   lowercase	:      Tuple		):





        """simple docstring"""




        lowercase_    :str					    =				20
        lowercase_    :Union[str, Any]					    =				model_class_name(lowercase		)

        lowercase_    :Tuple					    =				inputs_dict["input_ids"]
        lowercase_    :Optional[Any]					    =				inputs_dict["attention_mask"]

        lowercase_    :Union[str, Any]					    =				model.init_cache(input_ids.shape[0]			,   lowercase		)
        lowercase_    :Union[str, Any]					    =				jnp.ones((input_ids.shape[0], max_length)			,   dtype="i4"		)

        lowercase_    :Dict					    =				jnp.broadcast_to(
            jnp.arange(input_ids.shape[-1] - 1		)[None, :]			,   (input_ids.shape[0], input_ids.shape[-1] - 1)			,   )
        lowercase_    :Union[str, Any]					    =				model(
            input_ids[:, :-1]			,   attention_mask=lowercase			,   past_key_values=lowercase			,   position_ids=lowercase			,   )

        lowercase_    :Dict					    =				jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]			,   dtype="i4"		)
        lowercase_    :List[str]					    =				model(
            input_ids[:, -1:]			,   attention_mask=lowercase			,   past_key_values=outputs_cache.past_key_values			,   position_ids=lowercase			,   )

        lowercase_    :Optional[int]					    =				model(lowercase		)

        lowercase_    :Optional[int]					    =				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	:      str			,   lowercase	:      Dict			,   lowercase	:      str			,   lowercase	:      Dict		):





        """simple docstring"""




        lowercase_    :Optional[Any]					    =				20
        lowercase_    :List[Any]					    =				model_class_name(lowercase		)

        lowercase_    ,							lowercase_    :Tuple					    =				(
            inputs_dict["input_ids"],
            inputs_dict["attention_mask"],
        )

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

        lowercase_    :List[Any]					    =				model.init_cache(input_ids.shape[0]			,   lowercase		)
        lowercase_    :str					    =				jnp.broadcast_to(
            jnp.arange(input_ids.shape[-1] - 1		)[None, :]			,   (input_ids.shape[0], input_ids.shape[-1] - 1)			,   )

        lowercase_    :List[str]					    =				model(
            input_ids[:, :-1]			,   attention_mask=lowercase			,   past_key_values=lowercase			,   position_ids=lowercase			,   )
        lowercase_    :str					    =				jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]			,   dtype="i4"		)
        lowercase_    :int					    =				model(
            input_ids[:, -1:]			,   past_key_values=outputs_cache.past_key_values			,   attention_mask=lowercase			,   position_ids=lowercase			,   )

        lowercase_    :str					    =				model(lowercase			,   attention_mask=lowercase		)

        lowercase_    :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}'		)







@require_flax
class    a_     (       _lowerCAmelCase			,		unittest.TestCase			,		_lowerCAmelCase							):
     __A  				=    (FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
     __A  				=    () if is_flax_available() else ()





     def 							lowercase__   (				self	:      Optional[Any]		):





        """simple docstring"""




        lowercase_    :List[Any]					    =				FlaxOPTModelTester(self		)





     def 							lowercase__   (				self	:      int		):





        """simple docstring"""




        lowercase_    ,							lowercase_    :List[Any]					    =				self.model_tester.prepare_config_and_inputs()
        for model_class in self.all_model_classes:
           self.model_tester.check_use_cache_forward(lowercase			,   lowercase			,   lowercase		)





     def 							lowercase__   (				self	:      List[str]		):





        """simple docstring"""




        lowercase_    ,							lowercase_    :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(lowercase			,   lowercase			,   lowercase		)







     @slow
     def 							lowercase__   (				self	:      Optional[Any]		):





        """simple docstring"""




        for model_class_name in self.all_model_classes:
           lowercase_    :Union[str, Any]					    =				model_class_name.from_pretrained("facebook/opt-125m"		)
           lowercase_    :List[str]					    =				np.ones((1, 1)		) * model.config.eos_token_id
           lowercase_    :Optional[Any]					    =				model(lowercase		)
           self.assertIsNotNone(lowercase		)







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





     @slow
     def 							lowercase__   (				self	:      List[str]		):





        """simple docstring"""




        lowercase_    :List[str]					    =				FlaxOPTModel.from_pretrained("facebook/opt-350m"		)
        lowercase_    :Any					    =				jnp.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]		)
        lowercase_    :List[Any]					    =				model(input_ids=lowercase		).last_hidden_state
        lowercase_    :List[str]					    =				(1, 11, 512)
        self.assertEqual(output.shape			,   lowercase		)
        lowercase_    :Any					    =				jnp.array(
            [[-0.28_67, -1.92_56, -0.30_62], [-1.27_11, -0.13_37, -0.18_97], [0.41_09, 0.11_87, -1.31_42]]		)
        self.assertTrue(jnp.allclose(output[:, :3, :3]			,   lowercase			,   atol=4e-2		)		)







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





     def 							lowercase__   (				self	:      List[str]		):





        """simple docstring"""




        super().setUp()
        lowercase_    :str					    =				"facebook/opt-350m"







     def 							lowercase__   (				self	:      Tuple		):





        """simple docstring"""




        lowercase_    :List[str]					    =				FlaxOPTForCausalLM.from_pretrained(self.path_model		)
        lowercase_    :Tuple					    =				GPTaTokenizer.from_pretrained(self.path_model		)

        lowercase_    :Optional[Any]					    =				[
            "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
        lowercase_    :int					    =				tokenizer(lowercase			,   return_tensors="jax"			,   padding=lowercase			,   add_special_tokens=lowercase		)
        lowercase_    :str					    =				model(inputs.input_ids			,   attention_mask=inputs.attention_mask		)[0].mean(axis=-1		)
        lowercase_    :Optional[int]					    =				jnp.array(
            [
                [1.38_51, -13.89_23, -10.52_29, -10.75_33, -0.23_09, -10.23_84, -0.53_65, -9.09_47, -5.16_70],
                [-4.70_73, -10.62_76, -3.94_15, -21.52_42, -0.28_22, -0.28_22, -0.28_22, -0.28_22, -0.28_22],
                [0.62_47, -3.42_29, -8.91_79, -1.42_97, -14.16_50, 1.41_46, -9.02_18, -0.27_03, -0.27_03],
                [6.47_83, -1.99_13, -10.79_26, -2.33_36, 1.50_92, -0.99_74, -6.82_13, 1.34_77, 1.34_77],
            ]		)
        self.assertTrue(jnp.allclose(lowercase			,   lowercase			,   atol=4e-2		)		)

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







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





     @property
     def 							lowercase__   (				self	:      Optional[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 							lowercase__   (				self	:      Any		):





        """simple docstring"""




        lowercase_    :List[str]					    =				"facebook/opt-125m"

        lowercase_    :Dict					    =				[
            "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",
        ]

        lowercase_    :Any					    =				[]

        lowercase_    :int					    =				FlaxOPTForCausalLM.from_pretrained(lowercase		)
        lowercase_    :Optional[Any]					    =				GPTaTokenizer.from_pretrained(lowercase		)

        for prompt in self.prompts:
           lowercase_    :List[Any]					    =				tokenizer(lowercase			,   return_tensors="jax"		).input_ids

           lowercase_    :List[Any]					    =				model.generate(lowercase			,   max_length=10		)
           lowercase_    :Optional[int]					    =				generated_ids[0]

           lowercase_    :Any					    =				tokenizer.batch_decode(lowercase			,   skip_special_tokens=lowercase		)
           predicted_outputs += generated_string

        self.assertListEqual(lowercase			,   lowercase		)





     def 							lowercase__   (				self	:      Optional[Any]		):





        """simple docstring"""




        lowercase_    :Optional[Any]					    =				"facebook/opt-350m"

        lowercase_    :Union[str, Any]					    =				[
            "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",
        ]

        lowercase_    :Dict					    =				[]
        lowercase_    :List[Any]					    =				FlaxOPTForCausalLM.from_pretrained(lowercase		)
        lowercase_    :Dict					    =				GPTaTokenizer.from_pretrained(lowercase		)

        for prompt in self.prompts:
           lowercase_    :List[Any]					    =				tokenizer(lowercase			,   return_tensors="jax"		).input_ids

           lowercase_    :Dict					    =				model.generate(lowercase			,   max_length=10		)
           lowercase_    :Tuple					    =				generated_ids[0]

           lowercase_    :Dict					    =				tokenizer.batch_decode(lowercase			,   skip_special_tokens=lowercase		)
           predicted_outputs += generated_string

        self.assertListEqual(lowercase			,   lowercase		)





     def 							lowercase__   (				self	:      List[Any]		):





        """simple docstring"""




        lowercase_    :int					    =				"facebook/opt-125m"
        lowercase_    :int					    =				[
            "Today is a beautiful day and I want to thank",
            "In the city of Rome Canaver Canaver Canaver Canaver",
        ]
        lowercase_    :Optional[int]					    =				FlaxOPTForCausalLM.from_pretrained(lowercase		)
        lowercase_    :List[str]					    =				GPTaTokenizer.from_pretrained(lowercase		)
        lowercase_    :int					    =				tokenizer(
            [
                "Today is a beautiful day and I want to",
                "In the city of",
            ]			,   return_tensors="jax"			,   padding=lowercase			,   )

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

        lowercase_    :Optional[int]					    =				jit_generate(inputs["input_ids"]			,   attention_mask=inputs["attention_mask"]		).sequences

        lowercase_    :Union[str, Any]					    =				tokenizer.batch_decode(lowercase			,   skip_special_tokens=lowercase		)

        self.assertIsNotNone(lowercase			,   lowercase		)







     def 							lowercase__   (				self	:      Optional[int]		):





        """simple docstring"""




        lowercase_    :int					    =				"facebook/opt-350m"

        lowercase_    :str					    =				GPTaTokenizer.from_pretrained(lowercase		)
        lowercase_    :Optional[Any]					    =				FlaxOPTForCausalLM.from_pretrained(lowercase		)

        lowercase_    :int					    =				"left"

        # use different length sentences to test batching
        lowercase_    :Optional[int]					    =				[
            "Hello, my dog is a little",
            "Today, I",
        ]

        lowercase_    :Tuple					    =				tokenizer(lowercase			,   return_tensors="jax"			,   padding=lowercase		)
        lowercase_    :Any					    =				inputs["input_ids"]

        lowercase_    :str					    =				model.generate(input_ids=lowercase			,   attention_mask=inputs["attention_mask"]			,   trace=lowercase		)

        lowercase_    :Dict					    =				tokenizer(sentences[0]			,   return_tensors="jax"		).input_ids
        lowercase_    :List[Any]					    =				model.generate(input_ids=lowercase		)

        lowercase_    :str					    =				inputs_non_padded.shape[-1] - inputs["attention_mask"][-1].sum()
        lowercase_    :Optional[int]					    =				tokenizer(sentences[1]			,   return_tensors="jax"		).input_ids
        lowercase_    :List[Any]					    =				model.generate(input_ids=lowercase			,   max_length=model.config.max_length - num_paddings		)

        lowercase_    :str					    =				tokenizer.batch_decode(outputs[0]			,   skip_special_tokens=lowercase		)
        lowercase_    :Dict					    =				tokenizer.decode(output_non_padded[0][0]			,   skip_special_tokens=lowercase		)
        lowercase_    :int					    =				tokenizer.decode(output_padded[0][0]			,   skip_special_tokens=lowercase		)

        lowercase_    :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(lowercase			,   lowercase		)
        self.assertListEqual(lowercase			,   [non_padded_sentence, padded_sentence]		)









'''simple docstring'''


import math
def 							UpperCAmelCase_						(		__lowerCamelCase      :      int				):

   if 1 < number < 4:
      # 2 and 3 are primes
      return True
   elif number < 2 or number % 2 == 0 or number % 3 == 0:
      # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
      return False

   # All primes number are in format of 6k +/- 1
   for i in range(5			,int(math.sqrt(__lowerCamelCase				) + 1				)			,6				):
      if number % i == 0 or number % (i + 2) == 0:
         return False
   return True
def 							UpperCAmelCase_						(		__lowerCamelCase      :      int = 2_00_00_00				):
   return sum(num for num in range(3			,__lowerCamelCase			,2				) if is_prime(__lowerCamelCase				)				) + 2 if n > 2 else 0


if __name__ == "__main__":
 print(F'''{solution() = }''')


