





'''simple docstring'''




from __future__ import annotations

import unittest

import numpy as np

from transformers import OPTConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
  import tensorflow as tf

  from transformers import GPTaTokenizer, TFOPTForCausalLM, TFOPTModel
def 	UpperCAmelCase_							(					__lowercase							:      int			,     __lowercase							:      Dict			,     __lowercase							:      List[Any]=None			,     __lowercase							:      Tuple=None       )		->       List[Any]:


     '''simple docstring'''



     if attention_mask is None:
          _UpperCAmelCase     							=   tf.cast(tf.math.not_equal(lowerCamelCase__			,     config.pad_token_id       )			,     tf.inta       )
     return {"input_ids": input_ids, "attention_mask": attention_mask}







@require_tf
class   A_	:
 _lowerCamelCase  : Union[str, Any]           =							OPTConfig
 _lowerCamelCase  : int           =							{}
 _lowerCamelCase  : Union[str, Any]           =							'gelu'




 def __init__(					self       :    str	,			snake_case_       :    Optional[Any]	,			snake_case_       :    List[str]=1_3	,			snake_case_       :    int=7	,			snake_case_       :    Tuple=True	,			snake_case_       :    Union[str, Any]=False	,			snake_case_       :    Optional[Any]=9_9	,			snake_case_       :    List[Any]=1_6	,			snake_case_       :    Any=2	,			snake_case_       :    str=4	,			snake_case_       :    Any=4	,			snake_case_       :    Union[str, Any]="gelu"	,			snake_case_       :    Union[str, Any]=0.1	,			snake_case_       :    Optional[Any]=0.1	,			snake_case_       :    str=2_0	,			snake_case_       :    List[Any]=2	,			snake_case_       :    Union[str, Any]=1	,			snake_case_       :    Any=0	,			snake_case_       :    List[str]=1_6	,			snake_case_       :    Optional[Any]=1_6	,			):
      _UpperCAmelCase     							=   parent
      _UpperCAmelCase     							=   batch_size
      _UpperCAmelCase     							=   seq_length
      _UpperCAmelCase     							=   is_training
      _UpperCAmelCase     							=   use_labels
      _UpperCAmelCase     							=   vocab_size
      _UpperCAmelCase     							=   hidden_size
      _UpperCAmelCase     							=   num_hidden_layers
      _UpperCAmelCase     							=   num_attention_heads
      _UpperCAmelCase     							=   intermediate_size
      _UpperCAmelCase     							=   hidden_act
      _UpperCAmelCase     							=   hidden_dropout_prob
      _UpperCAmelCase     							=   attention_probs_dropout_prob
      _UpperCAmelCase     							=   max_position_embeddings
      _UpperCAmelCase     							=   eos_token_id
      _UpperCAmelCase     							=   pad_token_id
      _UpperCAmelCase     							=   bos_token_id
      _UpperCAmelCase     							=   embed_dim
      _UpperCAmelCase     							=   word_embed_proj_dim
      _UpperCAmelCase     							=   False




 def      lowercase	(					self       :    int    ):
      _UpperCAmelCase     							=   ids_tensor([self.batch_size, self.seq_length - 1]	,			self.vocab_size    )
      _UpperCAmelCase     							=   tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size    )	,			1    )
      _UpperCAmelCase     							=   tf.concat([input_ids, eos_tensor]	,			axis=1    )

      _UpperCAmelCase     							=   self.config_cls(
          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	,			word_embed_proj_dim=self.word_embed_proj_dim	,			is_encoder_decoder=snake_case_	,			**self.config_updates	,			)
      _UpperCAmelCase     							=   prepare_opt_inputs_dict(snake_case_	,			snake_case_    )
      return config, inputs_dict




 def      lowercase	(					self       :    Dict	,			snake_case_       :    Optional[int]	,			snake_case_       :    Dict    ):
      _UpperCAmelCase     							=   TFOPTModel(config=snake_case_    )
      _UpperCAmelCase     							=   inputs_dict["""input_ids"""]

      _UpperCAmelCase     							=   input_ids[:1, :]
      _UpperCAmelCase     							=   inputs_dict["""attention_mask"""][:1, :]
      _UpperCAmelCase     							=   1

      # first forward pass
      _UpperCAmelCase     							=   model(snake_case_	,			attention_mask=snake_case_	,			use_cache=snake_case_    )

      _UpperCAmelCase     							=   outputs.to_tuple()

      # create hypothetical next token and extent to next_input_ids
      _UpperCAmelCase     							=   ids_tensor((self.batch_size, 3)	,			config.vocab_size    )
      _UpperCAmelCase     							=   tf.cast(ids_tensor((self.batch_size, 3)	,			2    )	,			tf.inta    )

      # append to next input_ids and
      _UpperCAmelCase     							=   tf.concat([input_ids, next_tokens]	,			axis=-1    )
      _UpperCAmelCase     							=   tf.concat([attention_mask, next_attn_mask]	,			axis=-1    )

      _UpperCAmelCase     							=   model(snake_case_	,			attention_mask=snake_case_    )[0]
      _UpperCAmelCase     							=   model(snake_case_	,			attention_mask=snake_case_	,			past_key_values=snake_case_    )[0]

      self.parent.assertEqual(next_tokens.shape[1]	,			output_from_past.shape[1]    )

      # select random slice
      _UpperCAmelCase     							=   int(ids_tensor((1,)	,			output_from_past.shape[-1]    )    )
      _UpperCAmelCase     							=   output_from_no_past[:, -3:, random_slice_idx]
      _UpperCAmelCase     							=   output_from_past[:, :, random_slice_idx]

      # test that outputs are equal for slice
      tf.debugging.assert_near(snake_case_	,			snake_case_	,			rtol=1e-3    )







@require_tf
class   A_	(  __UpperCAmelCase							,						__UpperCAmelCase							,						unittest.TestCase			):
 _lowerCamelCase  : List[str]           =							(TFOPTModel, TFOPTForCausalLM) if is_tf_available() else ()
 _lowerCamelCase  : List[str]           =							(TFOPTForCausalLM,) if is_tf_available() else ()
 _lowerCamelCase  : List[Any]           =							(
     {'feature-extraction': TFOPTModel, 'text-generation': TFOPTForCausalLM} if is_tf_available() else {}
 )
 _lowerCamelCase  : Tuple           =							False
 _lowerCamelCase  : List[Any]           =							False
 _lowerCamelCase  : Any           =							False
 _lowerCamelCase  : List[Any]           =							10




 def      lowercase	(					self       :    int    ):
      _UpperCAmelCase     							=   TFOPTModelTester(self    )
      _UpperCAmelCase     							=   ConfigTester(self	,			config_class=snake_case_    )




 def      lowercase	(					self       :    Any    ):
      self.config_tester.run_common_tests()




 def      lowercase	(					self       :    List[Any]    ):
      _UpperCAmelCase     							=   self.model_tester.prepare_config_and_inputs_for_common()
      self.model_tester.check_decoder_model_past_large_inputs(*snake_case_    )




 def      lowercase	(					self       :    Any    ):
      _UpperCAmelCase     							=   self.model_tester.prepare_config_and_inputs_for_common()

      def _get_word_embedding_weight(snake_case_       :    List[str]	,			snake_case_       :    Dict    ):
           if hasattr(snake_case_	,			"weight"    ):
                return embedding_layer.weight
           else:
                # Here we build the word embeddings weights if not exists.
                # And then we retry to get the attribute once built.
                model.build()
                if hasattr(snake_case_	,			"weight"    ):
                     return embedding_layer.weight
                else:
                     return None

      for model_class in self.all_model_classes:
           for size in [config.vocab_size - 1_0, config.vocab_size + 1_0]:
                # build the embeddings
                _UpperCAmelCase     							=   model_class(config=snake_case_    )
                _UpperCAmelCase     							=   _get_word_embedding_weight(snake_case_	,			model.get_input_embeddings()    )
                _UpperCAmelCase     							=   _get_word_embedding_weight(snake_case_	,			model.get_output_embeddings()    )

                # reshape the embeddings
                model.resize_token_embeddings(snake_case_    )
                _UpperCAmelCase     							=   _get_word_embedding_weight(snake_case_	,			model.get_input_embeddings()    )
                _UpperCAmelCase     							=   _get_word_embedding_weight(snake_case_	,			model.get_output_embeddings()    )

                # check that the resized embeddings size matches the desired size.
                _UpperCAmelCase     							=   size if size is not None else config.vocab_size

                self.assertEqual(new_input_embeddings.shape[0]	,			snake_case_    )

                # check that weights remain the same after resizing
                _UpperCAmelCase     							=   True
                for pa, pa in zip(old_input_embeddings.value()	,			new_input_embeddings.value()    ):
                     if tf.math.reduce_sum(tf.math.abs(pa - pa    )    ) > 0:
                          _UpperCAmelCase     							=   False
                self.assertTrue(snake_case_    )

                if old_output_embeddings is not None and new_output_embeddings is not None:
                     self.assertEqual(new_output_embeddings.shape[0]	,			snake_case_    )

                     _UpperCAmelCase     							=   True
                     for pa, pa in zip(old_output_embeddings.value()	,			new_output_embeddings.value()    ):
                          if tf.math.reduce_sum(tf.math.abs(pa - pa    )    ) > 0:
                               _UpperCAmelCase     							=   False
                     self.assertTrue(snake_case_    )





def 	UpperCAmelCase_							(					__lowercase							:      str       )		->       str:


     '''simple docstring'''



     return tf.constant(lowerCamelCase__			,     dtype=tf.intaa       )







@require_tf
class   A_	(  unittest.TestCase			):
 _lowerCamelCase  : Optional[Any]           =							99




 def      lowercase	(					self       :    Optional[Any]    ):
      _UpperCAmelCase     							=   tf.ones((4, 1)	,			dtype=tf.intaa    ) * 2
      _UpperCAmelCase     							=   tf.concat([ids_tensor((4, 6)	,			self.vocab_size - 3    ) + 3, eos_column_vector]	,			axis=1    )
      _UpperCAmelCase     							=   input_ids.shape[0]
      _UpperCAmelCase     							=   OPTConfig(
          vocab_size=self.vocab_size	,			hidden_size=2_4	,			num_hidden_layers=2	,			num_attention_heads=2	,			ffn_dim=3_2	,			max_position_embeddings=4_8	,			eos_token_id=2	,			pad_token_id=1	,			bos_token_id=0	,			)
      return config, input_ids, batch_size







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




 @slow
 def      lowercase	(					self       :    Union[str, Any]    ):
      _UpperCAmelCase     							=   TFOPTModel.from_pretrained("facebook/opt-350m"    )
      _UpperCAmelCase     							=   _long_tensor([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]    )
      _UpperCAmelCase     							=   tf.not_equal(snake_case_	,			model.config.pad_token_id    )
      with tf.GradientTape():
           _UpperCAmelCase     							=   model(input_ids=snake_case_	,			attention_mask=snake_case_    ).last_hidden_state
      _UpperCAmelCase     							=   (1, 1_1, 5_1_2)
      self.assertEqual(output.shape	,			snake_case_    )
      _UpperCAmelCase     							=   tf.constant(
          [[-0.2_8_7_3, -1.9_2_1_8, -0.3_0_3_3], [-1.2_7_1_0, -0.1_3_3_8, -0.1_9_0_2], [0.4_0_9_5, 0.1_2_1_4, -1.3_1_2_1]]    )
      self.assertTrue(np.allclose(output[:, :3, :3]	,			snake_case_	,			atol=4e-3    )    )

      _UpperCAmelCase     							=   tf.function(snake_case_	,			jit_compile=snake_case_    )
      _UpperCAmelCase     							=   xla_generate(snake_case_	,			snake_case_    )[0]
      self.assertTrue(np.allclose(output[:, :3, :3]	,			snake_case_	,			atol=4e-2    )    )







@require_tf
@slow
class   A_	(  unittest.TestCase			):




 def      lowercase	(					self       :    int    ):
      super().setUp()
      _UpperCAmelCase     							=   """facebook/opt-350m"""




 def      lowercase	(					self       :    Union[str, Any]    ):
      _UpperCAmelCase     							=   TFOPTForCausalLM.from_pretrained(self.path_model    )
      _UpperCAmelCase     							=   GPTaTokenizer.from_pretrained(self.path_model    )

      _UpperCAmelCase     							=   [
          """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
      _UpperCAmelCase     							=   tokenizer(snake_case_	,			return_tensors="tf"	,			padding=snake_case_	,			add_special_tokens=snake_case_    )
      _UpperCAmelCase     							=   tf.math.reduce_mean(model(inputs.input_ids	,			attention_mask=inputs.attention_mask    )[0]	,			axis=-1    )
      _UpperCAmelCase     							=   tf.constant(
          [
              [1.3_8_5_1, -1_3.8_9_2_3, -1_0.5_2_2_9, -1_0.7_5_3_3, -0.2_3_0_9, -1_0.2_3_8_4, -0.5_3_6_5, -9.0_9_4_7, -5.1_6_7_0],
              [-4.7_0_7_3, -1_0.6_2_7_6, -3.9_4_1_5, -2_1.5_2_4_2, -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, -1_4.1_6_5_0, 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, -1_0.7_9_2_6, -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(np.allclose(snake_case_	,			snake_case_	,			atol=1e-4    )    )

      _UpperCAmelCase     							=   tf.function(snake_case_	,			jit_compile=snake_case_    )
      _UpperCAmelCase     							=   tf.math.reduce_mean(xla_generate(inputs.input_ids	,			attention_mask=inputs.attention_mask    )[0]	,			axis=-1    )
      self.assertTrue(np.allclose(snake_case_	,			snake_case_	,			atol=1e-4    )    )







@require_tf
@slow
class   A_	(  unittest.TestCase			):




 @property
 def      lowercase	(					self       :    List[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       :    Union[str, Any]    ):
      _UpperCAmelCase     							=   """facebook/opt-125m"""

      _UpperCAmelCase     							=   [
          """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""",
      ]

      _UpperCAmelCase     							=   []
      _UpperCAmelCase     							=   GPTaTokenizer.from_pretrained(snake_case_    )
      _UpperCAmelCase     							=   TFOPTForCausalLM.from_pretrained(snake_case_    )

      for prompt in self.prompts:
           _UpperCAmelCase     							=   tokenizer(snake_case_	,			return_tensors="tf"    ).input_ids

           _UpperCAmelCase     							=   model.generate(snake_case_	,			max_length=1_0    )

           _UpperCAmelCase     							=   tokenizer.batch_decode(snake_case_	,			skip_special_tokens=snake_case_    )
           predicted_outputs += generated_string

      self.assertListEqual(snake_case_	,			snake_case_    )




 def      lowercase	(					self       :    Dict    ):
      _UpperCAmelCase     							=   """facebook/opt-350m"""

      _UpperCAmelCase     							=   GPTaTokenizer.from_pretrained(snake_case_    )
      _UpperCAmelCase     							=   TFOPTForCausalLM.from_pretrained(snake_case_    )

      _UpperCAmelCase     							=   """left"""

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

      _UpperCAmelCase     							=   tokenizer(snake_case_	,			return_tensors="tf"	,			padding=snake_case_    )
      _UpperCAmelCase     							=   inputs["""input_ids"""]

      _UpperCAmelCase     							=   model.generate(input_ids=snake_case_	,			attention_mask=inputs["attention_mask"]    )

      _UpperCAmelCase     							=   tokenizer(sentences[0]	,			return_tensors="tf"    ).input_ids
      _UpperCAmelCase     							=   model.generate(input_ids=snake_case_    )

      _UpperCAmelCase     							=   inputs_non_padded.shape[-1] - tf.math.reduce_sum(
          tf.cast(inputs["attention_mask"][-1]	,			tf.intaa    )    )
      _UpperCAmelCase     							=   tokenizer(sentences[1]	,			return_tensors="tf"    ).input_ids
      _UpperCAmelCase     							=   model.generate(input_ids=snake_case_	,			max_length=model.config.max_length - num_paddings    )

      _UpperCAmelCase     							=   tokenizer.batch_decode(snake_case_	,			skip_special_tokens=snake_case_    )
      _UpperCAmelCase     							=   tokenizer.decode(output_non_padded[0]	,			skip_special_tokens=snake_case_    )
      _UpperCAmelCase     							=   tokenizer.decode(output_padded[0]	,			skip_special_tokens=snake_case_    )

      _UpperCAmelCase     							=   [
          """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(snake_case_	,			snake_case_    )
      self.assertListEqual(snake_case_	,			[non_padded_sentence, padded_sentence]    )




 def      lowercase	(					self       :    Optional[int]    ):
      _UpperCAmelCase     							=   """facebook/opt-350m"""

      _UpperCAmelCase     							=   [
          """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""",
      ]

      _UpperCAmelCase     							=   []
      _UpperCAmelCase     							=   GPTaTokenizer.from_pretrained(snake_case_    )
      _UpperCAmelCase     							=   TFOPTForCausalLM.from_pretrained(snake_case_    )

      for prompt in self.prompts:
           _UpperCAmelCase     							=   tokenizer(snake_case_	,			return_tensors="tf"    ).input_ids

           _UpperCAmelCase     							=   model.generate(snake_case_	,			max_length=1_0    )

           _UpperCAmelCase     							=   tokenizer.batch_decode(snake_case_	,			skip_special_tokens=snake_case_    )
           predicted_outputs += generated_string

      self.assertListEqual(snake_case_	,			snake_case_    )









'''simple docstring'''




from __future__ import annotations

import math



def 	a			( lowerCamelCase__    ):
			'''simple docstring'''





			if num <= 0:
						A_	:						List[Any]	=     f'{num}: Invalid input, please enter a positive integer.'
						raise ValueError(lowerCamelCase__    )

			A_	:						Dict	=     [True] * (num + 1)
			A_	:						List[Any]	=     []
			A_	:						Tuple	=     2
			A_	:						Optional[int]	=     int(math.sqrt(lowerCamelCase__    )    )

			while start <= end:
						# If start is a prime
						if sieve[start] is True:
									prime.append(lowerCamelCase__    )

									# Set multiples of start be False
									for i in range(start * start						,      num + 1						,      lowerCamelCase__    ):
												if sieve[i] is True:
															A_	:						List[Any]	=     False

						start += 1

			for j in range(end + 1						,      num + 1    ):
						if sieve[j] is True:
									prime.append(lowerCamelCase__    )

			return prime


if __name__ == "__main__":
						print(prime_sieve(int(input('''Enter a positive integer: ''').strip())))