


'''simple docstring'''
from __future__ import annotations

import unittest

from transformers import BlenderbotSmallConfig, BlenderbotSmallTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property

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 TFAutoModelForSeqaSeqLM, TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel






@require_tf
class        lowerCAmelCase_  :
       __UpperCAmelCase					=BlenderbotSmallConfig
       __UpperCAmelCase					={}
       __UpperCAmelCase					='gelu'






       def __init__( self			,     _UpperCamelCase			,     _UpperCamelCase=13			,     _UpperCamelCase=7			,     _UpperCamelCase=True			,     _UpperCamelCase=False			,     _UpperCamelCase=99			,     _UpperCamelCase=32			,     _UpperCamelCase=2			,     _UpperCamelCase=4			,     _UpperCamelCase=37			,     _UpperCamelCase=0.1			,     _UpperCamelCase=0.1			,     _UpperCamelCase=20			,     _UpperCamelCase=2			,     _UpperCamelCase=1			,     _UpperCamelCase=0			,     )->      List[Any]:
            _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_dropout_prob
            _A				=							attention_probs_dropout_prob
            _A				=							max_position_embeddings
            _A				=							eos_token_id
            _A				=							pad_token_id
            _A				=							bos_token_id






       def   UpperCamelCase						( self   )->      str:
            _A				=							ids_tensor([self.batch_size, self.seq_length - 1]			,     self.vocab_size   )
            _A				=							tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size   )			,     1   )
            _A				=							tf.concat([input_ids, eos_tensor]			,     axis=1   )

            _A				=							ids_tensor([self.batch_size, self.seq_length]			,     self.vocab_size   )

            _A				=							self.config_cls(
                vocab_size=self.vocab_size			,     d_model=self.hidden_size			,     encoder_layers=self.num_hidden_layers			,     decoder_layers=self.num_hidden_layers			,     encoder_attention_heads=self.num_attention_heads			,     decoder_attention_heads=self.num_attention_heads			,     encoder_ffn_dim=self.intermediate_size			,     decoder_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_ids=[2]			,     bos_token_id=self.bos_token_id			,     pad_token_id=self.pad_token_id			,     decoder_start_token_id=self.pad_token_id			,     **self.config_updates			,     )
            _A				=							prepare_blenderbot_small_inputs_dict(_UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase   )
            return config, inputs_dict






       def   UpperCamelCase						( self			,     _UpperCamelCase			,     _UpperCamelCase   )->      Any:
            _A				=							TFBlenderbotSmallModel(config=_UpperCamelCase   ).get_decoder()
            _A				=							inputs_dict['input_ids']

            _A				=							input_ids[:1, :]
            _A				=							inputs_dict['attention_mask'][:1, :]
            _A				=							inputs_dict['head_mask']
            _A				=							1

            # first forward pass
            _A				=							model(_UpperCamelCase			,     attention_mask=_UpperCamelCase			,     head_mask=_UpperCamelCase			,     use_cache=_UpperCamelCase   )

            _A		,				_A				=							outputs.to_tuple()

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

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

            _A				=							model(_UpperCamelCase			,     attention_mask=_UpperCamelCase   )[0]
            _A				=							model(_UpperCamelCase			,     attention_mask=_UpperCamelCase			,     past_key_values=_UpperCamelCase   )[0]

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

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

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






def 					lowerCamelCase_ ( __UpperCamelCase					:       Tuple							,    __UpperCamelCase					:       str							,    __UpperCamelCase					:       Tuple							,    __UpperCamelCase					:       Union[str, Any]=None							,    __UpperCamelCase					:       Optional[Any]=None							,    __UpperCamelCase					:       Optional[int]=None							,    __UpperCamelCase					:       List[str]=None							,    __UpperCamelCase					:       int=None							,    )					->	str:







     """simple docstring"""
     if attention_mask is None:
          _A				=							tf.cast(tf.math.not_equal(__UpperCamelCase							,    config.pad_token_id		)							,    tf.inta		)
     if decoder_attention_mask is None:
          _A				=							tf.concat(
              [
                  tf.ones(decoder_input_ids[:, :1].shape							,    dtype=tf.inta		),
                  tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]							,    config.pad_token_id		)							,    tf.inta		),
              ]							,    axis=-1							,    )
     if head_mask is None:
          _A				=							tf.ones((config.encoder_layers, config.encoder_attention_heads)		)
     if decoder_head_mask is None:
          _A				=							tf.ones((config.decoder_layers, config.decoder_attention_heads)		)
     if cross_attn_head_mask is None:
          _A				=							tf.ones((config.decoder_layers, config.decoder_attention_heads)		)
     return {
         "input_ids": input_ids,
         "decoder_input_ids": decoder_input_ids,
         "attention_mask": attention_mask,
         "decoder_attention_mask": decoder_attention_mask,
         "head_mask": head_mask,
         "decoder_head_mask": decoder_head_mask,
         "cross_attn_head_mask": cross_attn_head_mask,
     }






@require_tf
class        lowerCAmelCase_  (     UpperCAmelCase      ,							UpperCAmelCase      ,							unittest.TestCase	):
       __UpperCAmelCase					=(
           (TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel) if is_tf_available() else ()
       )
       __UpperCAmelCase					=(TFBlenderbotSmallForConditionalGeneration,) if is_tf_available() else ()
       __UpperCAmelCase					=(
           {
               'conversational': TFBlenderbotSmallForConditionalGeneration,
               'feature-extraction': TFBlenderbotSmallModel,
               'summarization': TFBlenderbotSmallForConditionalGeneration,
               'text2text-generation': TFBlenderbotSmallForConditionalGeneration,
               'translation': TFBlenderbotSmallForConditionalGeneration,
           }
           if is_tf_available()
           else {}
       )
       __UpperCAmelCase					=True
       __UpperCAmelCase					=False
       __UpperCAmelCase					=False






       def   UpperCamelCase						( self   )->      List[Any]:
            _A				=							TFBlenderbotSmallModelTester(self   )
            _A				=							ConfigTester(self			,     config_class=_UpperCamelCase   )






       def   UpperCamelCase						( self   )->      Union[str, Any]:
            self.config_tester.run_common_tests()






       def   UpperCamelCase						( self   )->      Optional[Any]:
            _A				=							self.model_tester.prepare_config_and_inputs_for_common()
            self.model_tester.check_decoder_model_past_large_inputs(*_UpperCamelCase   )






@require_tokenizers
@require_tf
class        lowerCAmelCase_  (     unittest.TestCase	):
       __UpperCAmelCase					=[
           'Social anxiety\nWow, I am never shy. Do you have anxiety?\nYes. I end up sweating and blushing and feel like '
           '  i\'m going to throw up.\nand why is that?'
       ]
       __UpperCAmelCase					='facebook/blenderbot_small-90M'






       @cached_property
       def   UpperCamelCase						( self   )->      Tuple:
            # use "old" tokenizer here because of bug when downloading new tokenizer
            return BlenderbotSmallTokenizer.from_pretrained('facebook/blenderbot-90M'   )






       @cached_property
       def   UpperCamelCase						( self   )->      Optional[int]:
            _A				=							TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name   )
            return model






       @slow
       def   UpperCamelCase						( self   )->      List[Any]:
            _A				=							self.tokenizer(self.src_text			,     return_tensors='tf'   )
            _A				=							self.model.generate(
                model_inputs.input_ids			,     attention_mask=model_inputs.attention_mask			,     num_beams=2			,     use_cache=_UpperCamelCase			,     )
            _A				=							self.tokenizer.batch_decode(generated_ids.numpy()			,     skip_special_tokens=_UpperCamelCase   )[0]
            assert generated_words in (
                "i don't know. i just feel like i'm going to throw up. it's not fun.",
                "i'm not sure. i just feel like i've been feeling like i have to be in a certain place",
                "i'm not sure. i just feel like i've been in a bad situation.",
            )









'''simple docstring'''
def 					lowerCamelCase_ ( __UpperCamelCase					:       int		)					->	list:







     """simple docstring"""
     _A				=							int(__UpperCamelCase		)
     if n_element < 1:
          _A				=							ValueError('a should be a positive number'		)
          raise my_error

     _A				=							[1]
     _A		,				_A		,				_A				=							(0, 0, 0)
     _A				=							1
     while index < n_element:
          while hamming_list[i] * 2 <= hamming_list[-1]:
               i += 1
          while hamming_list[j] * 3 <= hamming_list[-1]:
               j += 1
          while hamming_list[k] * 5 <= hamming_list[-1]:
               k += 1
          hamming_list.append(
              min(hamming_list[i] * 2							,    hamming_list[j] * 3							,    hamming_list[k] * 5		)		)
          index += 1
     return hamming_list


if __name__ == "__main__":
    lowerCAmelCase						=					input("""Enter the last number (nth term) of the Hamming Number Series: """)
    print("""Formula of Hamming Number Series => 2^i * 3^j * 5^k""")
    lowerCAmelCase						=					hamming(int(n))
    print("""-----------------------------------------------------""")
    print(F'The list with nth numbers is: {hamming_numbers}')
    print("""-----------------------------------------------------""")





