
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 							A__		:
      _UpperCAmelCase				:    List[Any]           =			BlenderbotSmallConfig
      _UpperCAmelCase				:    Any           =			{}
      _UpperCAmelCase				:    Tuple           =			"""gelu"""
      def __init__(      self    ,  __magic_name__    ,  __magic_name__=1_3    ,  __magic_name__=7    ,  __magic_name__=True    ,  __magic_name__=False    ,  __magic_name__=9_9    ,  __magic_name__=3_2    ,  __magic_name__=2    ,  __magic_name__=4    ,  __magic_name__=3_7    ,  __magic_name__=0.1    ,  __magic_name__=0.1    ,  __magic_name__=2_0    ,  __magic_name__=2    ,  __magic_name__=1    ,  __magic_name__=0    ,  ):
       lowerCamelCase		:	List[str]							=      parent
       lowerCamelCase		:	Optional[int]							=      batch_size
       lowerCamelCase		:	str							=      seq_length
       lowerCamelCase		:	int							=      is_training
       lowerCamelCase		:	Optional[Any]							=      use_labels
       lowerCamelCase		:	List[Any]							=      vocab_size
       lowerCamelCase		:	Optional[Any]							=      hidden_size
       lowerCamelCase		:	Union[str, Any]							=      num_hidden_layers
       lowerCamelCase		:	Optional[int]							=      num_attention_heads
       lowerCamelCase		:	Union[str, Any]							=      intermediate_size

       lowerCamelCase		:	int							=      hidden_dropout_prob
       lowerCamelCase		:	Dict							=      attention_probs_dropout_prob
       lowerCamelCase		:	Optional[Any]							=      max_position_embeddings
       lowerCamelCase		:	str							=      eos_token_id
       lowerCamelCase		:	Dict							=      pad_token_id
       lowerCamelCase		:	Tuple							=      bos_token_id
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	Optional[Any]							=      ids_tensor([self.batch_size, self.seq_length - 1]    ,  self.vocab_size      )
       lowerCamelCase		:	Union[str, Any]							=      tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size      )    ,  1      )
       lowerCamelCase		:	List[str]							=      tf.concat([input_ids, eos_tensor]    ,  axis=1      )

       lowerCamelCase		:	Optional[int]							=      ids_tensor([self.batch_size, self.seq_length]    ,  self.vocab_size      )

       lowerCamelCase		:	Dict							=      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    ,  )
       lowerCamelCase		:	Any							=      prepare_blenderbot_small_inputs_dict(__magic_name__    ,  __magic_name__    ,  __magic_name__      )
       return config, inputs_dict






      def 						UpperCamelCase__							(      self    ,  __magic_name__    ,  __magic_name__      ):
       lowerCamelCase		:	List[Any]							=      TFBlenderbotSmallModel(config=__magic_name__      ).get_decoder()
       lowerCamelCase		:	List[str]							=      inputs_dict["""input_ids"""]

       lowerCamelCase		:	int							=      input_ids[:1, :]
       lowerCamelCase		:	str							=      inputs_dict["""attention_mask"""][:1, :]
       lowerCamelCase		:	Tuple							=      inputs_dict["""head_mask"""]
       lowerCamelCase		:	int							=      1

       # first forward pass
       lowerCamelCase		:	List[str]							=      model(__magic_name__    ,  attention_mask=__magic_name__    ,  head_mask=__magic_name__    ,  use_cache=__magic_name__      )

       lowerCamelCase			,    lowerCamelCase		:	int							=      outputs.to_tuple()

       # create hypothetical next token and extent to next_input_ids
       lowerCamelCase		:	str							=      ids_tensor((self.batch_size, 3)    ,  config.vocab_size      )
       lowerCamelCase		:	Union[str, Any]							=      tf.cast(ids_tensor((self.batch_size, 3)    ,  2      )    ,  tf.inta      )

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

       lowerCamelCase		:	int							=      model(__magic_name__    ,  attention_mask=__magic_name__      )[0]
       lowerCamelCase		:	Optional[int]							=      model(__magic_name__    ,  attention_mask=__magic_name__    ,  past_key_values=__magic_name__      )[0]

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

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

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





def     _a					(						lowerCamelCase,	lowerCamelCase,	lowerCamelCase,	lowerCamelCase=None,	lowerCamelCase=None,	lowerCamelCase=None,	lowerCamelCase=None,	lowerCamelCase=None,	):
 if attention_mask is None:
  lowerCamelCase		:	Union[str, Any]							=      tf.cast(tf.math.not_equal(lowerCamelCase,	config.pad_token_id							),	tf.inta							)
 if decoder_attention_mask is None:
  lowerCamelCase		:	Any							=      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:
  lowerCamelCase		:	List[Any]							=      tf.ones((config.encoder_layers, config.encoder_attention_heads)							)
 if decoder_head_mask is None:
  lowerCamelCase		:	Optional[Any]							=      tf.ones((config.decoder_layers, config.decoder_attention_heads)							)
 if cross_attn_head_mask is None:
  lowerCamelCase		:	str							=      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 							A__		(	__SCREAMING_SNAKE_CASE  ,   __SCREAMING_SNAKE_CASE  ,   unittest.TestCase):
      _UpperCAmelCase				:    str           =			(
          (TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel) if is_tf_available() else ()
      )
      _UpperCAmelCase				:    Any           =			(TFBlenderbotSmallForConditionalGeneration,) if is_tf_available() else ()
      _UpperCAmelCase				:    int           =			(
          {
              """conversational""": TFBlenderbotSmallForConditionalGeneration,
              """feature-extraction""": TFBlenderbotSmallModel,
              """summarization""": TFBlenderbotSmallForConditionalGeneration,
              """text2text-generation""": TFBlenderbotSmallForConditionalGeneration,
              """translation""": TFBlenderbotSmallForConditionalGeneration,
          }
          if is_tf_available()
          else {}
      )
      _UpperCAmelCase				:    List[Any]           =			True
      _UpperCAmelCase				:    str           =			False
      _UpperCAmelCase				:    Any           =			False
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	int							=      TFBlenderbotSmallModelTester(self      )
       lowerCamelCase		:	List[str]							=      ConfigTester(self    ,  config_class=__magic_name__      )
      def 						UpperCamelCase__							(      self      ):
       self.config_tester.run_common_tests()






      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	Any							=      self.model_tester.prepare_config_and_inputs_for_common()
       self.model_tester.check_decoder_model_past_large_inputs(*__magic_name__      )





@require_tokenizers
@require_tf
class 							A__		(	unittest.TestCase):
      _UpperCAmelCase				:    int           =			[
          """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				:    Tuple           =			"""facebook/blenderbot_small-90M"""
      @cached_property
      def 						UpperCamelCase__							(      self      ):
       # use "old" tokenizer here because of bug when downloading new tokenizer
       return BlenderbotSmallTokenizer.from_pretrained("""facebook/blenderbot-90M"""      )
      @cached_property
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	Tuple							=      TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name      )
       return model






      @slow
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	List[str]							=      self.tokenizer(self.src_text    ,  return_tensors="""tf"""      )
       lowerCamelCase		:	Dict							=      self.model.generate(
           model_inputs.input_ids    ,  attention_mask=model_inputs.attention_mask    ,  num_beams=2    ,  use_cache=__magic_name__    ,  )
       lowerCamelCase		:	Any							=      self.tokenizer.batch_decode(generated_ids.numpy()    ,  skip_special_tokens=__magic_name__      )[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.",
       )






def     _a					(						lowerCamelCase = 100_0000							):
 lowerCamelCase		:	Any							=      set(range(3,	lowerCamelCase,	2							)							)
 primes.add(2							)
 for p in range(3,	lowerCamelCase,	2							):
  if p not in primes:
   continue
  primes.difference_update(set(range(p * p,	lowerCamelCase,	lowerCamelCase							)							)							)

 lowerCamelCase		:	Any							=      [float(lowerCamelCase							) for n in range(limit + 1							)]

 for p in primes:
  for n in range(lowerCamelCase,	limit + 1,	lowerCamelCase							):
   phi[n] *= 1 - 1 / p

 return int(sum(phi[2:]							)							)


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




