




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__ :   List[Any]						=     'platform'

       import jax
       import jax.numpy as jnp

       from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer

def   A   (   _SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE=None				,_SCREAMING_SNAKE_CASE=None					)			->				str:
       if attention_mask is None:
              lowerCamelCase     :   Tuple    				=      np.where(input_ids != config.pad_token_id				,1				,0					)
       return {
           "input_ids": input_ids,
           "attention_mask": attention_mask,
       }




@require_flax
class 	UpperCamelCase__			:

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







    def 					_lowercase  (					self  )  ->   Tuple:
           lowerCamelCase     :   Dict    				=      np.clip(ids_tensor([self.batch_size, self.seq_length - 1]  ,						self.vocab_size  )  ,						3  ,						self.vocab_size  )
           lowerCamelCase     :   Tuple    				=      np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)  ,						dtype=np.intaa  ))  ,						-1  )

           lowerCamelCase     :   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__  ,						)
           lowerCamelCase     :   Optional[Any]    				=      prepare_opt_inputs_dict(UpperCamelCase__  ,						UpperCamelCase__  )
           return config, inputs_dict







    def 					_lowercase  (					self  )  ->   int:
           lowerCamelCase				,   lowerCamelCase     :   Optional[int]    				=      self.prepare_config_and_inputs()
           return config, inputs_dict







    def 					_lowercase  (					self  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  )  ->   int:
           lowerCamelCase     :   Optional[Any]    				=      20
           lowerCamelCase     :   Optional[Any]    				=      model_class_name(UpperCamelCase__  )

           lowerCamelCase     :   List[Any]    				=      inputs_dict["input_ids"]
           lowerCamelCase     :   Union[str, Any]    				=      inputs_dict["attention_mask"]

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

           lowerCamelCase     :   Dict    				=      jnp.broadcast_to(
               jnp.arange(input_ids.shape[-1] - 1  )[None, :]  ,						(input_ids.shape[0], input_ids.shape[-1] - 1)  ,						)
           lowerCamelCase     :   Optional[int]    				=      model(
               input_ids[:, :-1]  ,						attention_mask=UpperCamelCase__  ,						past_key_values=UpperCamelCase__  ,						position_ids=UpperCamelCase__  ,						)

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

           lowerCamelCase     :   Dict    				=      model(UpperCamelCase__  )

           lowerCamelCase     :   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  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  )  ->   Union[str, Any]:
           lowerCamelCase     :   int    				=      20
           lowerCamelCase     :   Optional[Any]    				=      model_class_name(UpperCamelCase__  )

           lowerCamelCase				,   lowerCamelCase     :   Union[str, Any]    				=      (
               inputs_dict["input_ids"],
               inputs_dict["attention_mask"],
           )

           lowerCamelCase     :   Union[str, Any]    				=      jnp.concatenate(
               [
                   attention_mask,
                   jnp.zeros((attention_mask.shape[0], max_length - attention_mask.shape[1])  ),
               ]  ,						axis=-1  ,						)

           lowerCamelCase     :   int    				=      model.init_cache(input_ids.shape[0]  ,						UpperCamelCase__  )
           lowerCamelCase     :   str    				=      jnp.broadcast_to(
               jnp.arange(input_ids.shape[-1] - 1  )[None, :]  ,						(input_ids.shape[0], input_ids.shape[-1] - 1)  ,						)

           lowerCamelCase     :   Tuple    				=      model(
               input_ids[:, :-1]  ,						attention_mask=UpperCamelCase__  ,						past_key_values=UpperCamelCase__  ,						position_ids=UpperCamelCase__  ,						)
           lowerCamelCase     :   Dict    				=      jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]  ,						dtype="i4"  )
           lowerCamelCase     :   Union[str, Any]    				=      model(
               input_ids[:, -1:]  ,						past_key_values=outputs_cache.past_key_values  ,						attention_mask=UpperCamelCase__  ,						position_ids=UpperCamelCase__  ,						)

           lowerCamelCase     :   Any    				=      model(UpperCamelCase__  ,						attention_mask=UpperCamelCase__  )

           lowerCamelCase     :   List[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 	UpperCamelCase__			(lowerCAmelCase__					,       unittest.TestCase					,       lowerCAmelCase__  ):

    '''simple docstring'''



    lowerCamelCase_   :			List[str]			  =   (FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
    lowerCamelCase_   :			str			  =   () if is_flax_available() else ()







    def 					_lowercase  (					self  )  ->   Optional[int]:
           lowerCamelCase     :   str    				=      FlaxOPTModelTester(self  )







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




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

    '''simple docstring'''



    @slow
    def 					_lowercase  (					self  )  ->   Optional[Any]:
           lowerCamelCase     :   Optional[Any]    				=      FlaxOPTModel.from_pretrained("facebook/opt-350m"  )
           lowerCamelCase     :   Any    				=      jnp.array([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]  )
           lowerCamelCase     :   Any    				=      model(input_ids=UpperCamelCase__  ).last_hidden_state
           lowerCamelCase     :   Union[str, Any]    				=      (1, 11, 512)
           self.assertEqual(output.shape  ,						UpperCamelCase__  )
           lowerCamelCase     :   List[str]    				=      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 	UpperCamelCase__			(unittest.TestCase  ):

    '''simple docstring'''



    def 					_lowercase  (					self  )  ->   int:
           super().setUp()
           lowerCamelCase     :   Any    				=      "facebook/opt-350m"







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

           lowerCamelCase     :   int    				=      [
               "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
           lowerCamelCase     :   Union[str, Any]    				=      tokenizer(UpperCamelCase__  ,						return_tensors="jax"  ,						padding=UpperCamelCase__  ,						add_special_tokens=UpperCamelCase__  )
           lowerCamelCase     :   int    				=      model(inputs.input_ids  ,						attention_mask=inputs.attention_mask  )[0].mean(axis=-1  )
           lowerCamelCase     :   Any    				=      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  )  )

           lowerCamelCase     :   int    				=      jax.jit(UpperCamelCase__  )
           lowerCamelCase     :   Optional[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 	UpperCamelCase__			(unittest.TestCase  ):

    '''simple docstring'''



    @property
    def 					_lowercase  (					self  )  ->   Optional[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 					_lowercase  (					self  )  ->   Optional[int]:
           lowerCamelCase     :   Tuple    				=      "facebook/opt-125m"

           lowerCamelCase     :   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",
           ]

           lowerCamelCase     :   str    				=      []

           lowerCamelCase     :   Optional[Any]    				=      FlaxOPTForCausalLM.from_pretrained(UpperCamelCase__  )
           lowerCamelCase     :   List[Any]    				=      GPTaTokenizer.from_pretrained(UpperCamelCase__  )

           for prompt in self.prompts:
                  lowerCamelCase     :   Any    				=      tokenizer(UpperCamelCase__  ,						return_tensors="jax"  ).input_ids

                  lowerCamelCase     :   List[Any]    				=      model.generate(UpperCamelCase__  ,						max_length=10  )
                  lowerCamelCase     :   str    				=      generated_ids[0]

                  lowerCamelCase     :   List[str]    				=      tokenizer.batch_decode(UpperCamelCase__  ,						skip_special_tokens=UpperCamelCase__  )
                  predicted_outputs += generated_string

           self.assertListEqual(UpperCamelCase__  ,						UpperCamelCase__  )







    def 					_lowercase  (					self  )  ->   List[str]:
           lowerCamelCase     :   Dict    				=      "facebook/opt-350m"

           lowerCamelCase     :   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",
           ]

           lowerCamelCase     :   Dict    				=      []
           lowerCamelCase     :   str    				=      FlaxOPTForCausalLM.from_pretrained(UpperCamelCase__  )
           lowerCamelCase     :   Union[str, Any]    				=      GPTaTokenizer.from_pretrained(UpperCamelCase__  )

           for prompt in self.prompts:
                  lowerCamelCase     :   Union[str, Any]    				=      tokenizer(UpperCamelCase__  ,						return_tensors="jax"  ).input_ids

                  lowerCamelCase     :   Dict    				=      model.generate(UpperCamelCase__  ,						max_length=10  )
                  lowerCamelCase     :   Dict    				=      generated_ids[0]

                  lowerCamelCase     :   Dict    				=      tokenizer.batch_decode(UpperCamelCase__  ,						skip_special_tokens=UpperCamelCase__  )
                  predicted_outputs += generated_string

           self.assertListEqual(UpperCamelCase__  ,						UpperCamelCase__  )







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

           lowerCamelCase     :   Optional[Any]    				=      jax.jit(model.generate  )

           lowerCamelCase     :   Tuple    				=      jit_generate(inputs["input_ids"]  ,						attention_mask=inputs["attention_mask"]  ).sequences

           lowerCamelCase     :   Optional[int]    				=      tokenizer.batch_decode(UpperCamelCase__  ,						skip_special_tokens=UpperCamelCase__  )

           self.assertIsNotNone(UpperCamelCase__  ,						UpperCamelCase__  )







    def 					_lowercase  (					self  )  ->   Optional[Any]:
           lowerCamelCase     :   Optional[int]    				=      "facebook/opt-350m"

           lowerCamelCase     :   List[str]    				=      GPTaTokenizer.from_pretrained(UpperCamelCase__  )
           lowerCamelCase     :   Union[str, Any]    				=      FlaxOPTForCausalLM.from_pretrained(UpperCamelCase__  )

           lowerCamelCase     :   Any    				=      "left"

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

           lowerCamelCase     :   Optional[int]    				=      tokenizer(UpperCamelCase__  ,						return_tensors="jax"  ,						padding=UpperCamelCase__  )
           lowerCamelCase     :   str    				=      inputs["input_ids"]

           lowerCamelCase     :   List[str]    				=      model.generate(input_ids=UpperCamelCase__  ,						attention_mask=inputs["attention_mask"]  ,						trace=UpperCamelCase__  )

           lowerCamelCase     :   Any    				=      tokenizer(sentences[0]  ,						return_tensors="jax"  ).input_ids
           lowerCamelCase     :   Optional[int]    				=      model.generate(input_ids=UpperCamelCase__  )

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

           lowerCamelCase     :   Union[str, Any]    				=      tokenizer.batch_decode(outputs[0]  ,						skip_special_tokens=UpperCamelCase__  )
           lowerCamelCase     :   str    				=      tokenizer.decode(output_non_padded[0][0]  ,						skip_special_tokens=UpperCamelCase__  )
           lowerCamelCase     :   List[str]    				=      tokenizer.decode(output_padded[0][0]  ,						skip_special_tokens=UpperCamelCase__  )

           lowerCamelCase     :   str    				=      [
               "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]  )












from itertools import compress, repeat
from math import ceil, sqrt

def   A   (   _SCREAMING_SNAKE_CASE					)			->				list[int]:

       if num <= 2:
              return []
       if num == 3:
              return [2]

       # Odd sieve for numbers in range [3, num - 1]
       lowerCamelCase     :   str    				=      bytearray(B"\x01"					) * ((num >> 1) - 1)

       for i in range(3				,int(sqrt(_SCREAMING_SNAKE_CASE					)					) + 1				,2					):
              if sieve[(i >> 1) - 1]:
                     lowerCamelCase     :   Tuple    				=      i**2
                     lowerCamelCase     :   str    				=      repeat(
                         0				,ceil((num - i_squared) / (i << 1)					)					)

       return [2] + list(compress(range(3				,_SCREAMING_SNAKE_CASE				,2					)				,_SCREAMING_SNAKE_CASE					)					)


if __name__ == "__main__":
       import doctest

       doctest.testmod()






