"""simple docstring"""


from __future__ import annotations

import unittest

from transformers import BlenderbotConfig, BlenderbotTokenizer, 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, TFBlenderbotForConditionalGeneration, TFBlenderbotModel




@require_tf
class  UpperCamelCase					:
       SCREAMING_SNAKE_CASE_			=      BlenderbotConfig
       SCREAMING_SNAKE_CASE_			=      {}
       SCREAMING_SNAKE_CASE_			=      "gelu"







       def __init__( self,							lowerCAmelCase__,							lowerCAmelCase__=13,							lowerCAmelCase__=7,							lowerCAmelCase__=True,							lowerCAmelCase__=False,							lowerCAmelCase__=99,							lowerCAmelCase__=32,							lowerCAmelCase__=2,							lowerCAmelCase__=4,							lowerCAmelCase__=37,							lowerCAmelCase__=0.1,							lowerCAmelCase__=0.1,							lowerCAmelCase__=20,							lowerCAmelCase__=2,							lowerCAmelCase__=1,							lowerCAmelCase__=0,							)      ->      Optional[int]:
            snake_case_					       =					parent
            snake_case_					       =					batch_size
            snake_case_					       =					seq_length
            snake_case_					       =					is_training
            snake_case_					       =					use_labels
            snake_case_					       =					vocab_size
            snake_case_					       =					hidden_size
            snake_case_					       =					num_hidden_layers
            snake_case_					       =					num_attention_heads
            snake_case_					       =					intermediate_size

            snake_case_					       =					hidden_dropout_prob
            snake_case_					       =					attention_probs_dropout_prob
            snake_case_					       =					max_position_embeddings
            snake_case_					       =					eos_token_id
            snake_case_					       =					pad_token_id
            snake_case_					       =					bos_token_id







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

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

            snake_case_					       =					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,							)
            snake_case_					       =					prepare_blenderbot_inputs_dict(lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__)
            return config, inputs_dict







       def 		a_						( self,							lowerCAmelCase__,							lowerCAmelCase__)      ->      Dict:
            snake_case_					       =					TFBlenderbotModel(config=lowerCAmelCase__).get_decoder()
            snake_case_					       =					inputs_dict['input_ids']

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

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

            snake_case_       ,	snake_case_					       =					outputs.to_tuple()

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

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

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

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

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

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







def 					UpperCAmelCase    (		UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase=None						,					UpperCAmelCase=None						,					UpperCAmelCase=None						,					UpperCAmelCase=None						,					UpperCAmelCase=None						,					)				->      Optional[Any]:
     if attention_mask is None:
          snake_case_					       =					tf.cast(tf.math.not_equal(UpperCAmelCase						,					config.pad_token_id					)						,					tf.inta					)
     if decoder_attention_mask is None:
          snake_case_					       =					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:
          snake_case_					       =					tf.ones((config.encoder_layers, config.encoder_attention_heads)					)
     if decoder_head_mask is None:
          snake_case_					       =					tf.ones((config.decoder_layers, config.decoder_attention_heads)					)
     if cross_attn_head_mask is None:
          snake_case_					       =					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  UpperCamelCase					(				lowerCAmelCase__    ,		lowerCAmelCase__    ,		unittest.TestCase   ):
       SCREAMING_SNAKE_CASE_			=      (TFBlenderbotForConditionalGeneration, TFBlenderbotModel) if is_tf_available() else ()
       SCREAMING_SNAKE_CASE_			=      (TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
       SCREAMING_SNAKE_CASE_			=      (
           {
               "conversational": TFBlenderbotForConditionalGeneration,
               "feature-extraction": TFBlenderbotModel,
               "summarization": TFBlenderbotForConditionalGeneration,
               "text2text-generation": TFBlenderbotForConditionalGeneration,
               "translation": TFBlenderbotForConditionalGeneration,
           }
           if is_tf_available()
           else {}
       )
       SCREAMING_SNAKE_CASE_			=      True
       SCREAMING_SNAKE_CASE_			=      False
       SCREAMING_SNAKE_CASE_			=      False







       def 		a_						( self)      ->      Union[str, Any]:
            snake_case_					       =					TFBlenderbotModelTester(self)
            snake_case_					       =					ConfigTester(self,							config_class=lowerCAmelCase__)







       def 		a_						( self)      ->      Dict:
            self.config_tester.run_common_tests()







       def 		a_						( self)      ->      Tuple:
            snake_case_					       =					self.model_tester.prepare_config_and_inputs_for_common()
            self.model_tester.check_decoder_model_past_large_inputs(*lowerCAmelCase__)




@require_tokenizers
@require_tf
class  UpperCamelCase					(				unittest.TestCase   ):
       SCREAMING_SNAKE_CASE_			=      ["My friends are cool but they eat too many carbs."]
       SCREAMING_SNAKE_CASE_			=      "facebook/blenderbot-400M-distill"







       @cached_property
       def 		a_						( self)      ->      Union[str, Any]:
            return BlenderbotTokenizer.from_pretrained(self.model_name)







       @cached_property
       def 		a_						( self)      ->      Union[str, Any]:
            snake_case_					       =					TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name)
            return model







       @slow
       def 		a_						( self)      ->      Dict:
            snake_case_					       =					self.tokenizer(self.src_text,							return_tensors='tf')
            snake_case_					       =					self.model.generate(
                model_inputs.input_ids,							)
            snake_case_					       =					self.tokenizer.batch_decode(generated_ids.numpy(),							skip_special_tokens=lowerCAmelCase__)[0]
            assert (
                generated_words
                == " That's unfortunate. Are they trying to lose weight or are they just trying to be healthier?"
            )








def 		lowerCAmelCase_    (  __UpperCAmelCase:			int = 100_0000      )      ->	int:
    UpperCamelCase__							:				str          =      limit + 1
    UpperCamelCase__							:				List[str]          =      [0] * limit
    for first_term in range(1		,   __UpperCAmelCase      ):
        for n in range(__UpperCAmelCase		,   __UpperCAmelCase		,   __UpperCAmelCase      ):
            UpperCamelCase__							:				str          =      first_term + n / first_term
            if common_difference % 4:  # d must be divisble by 4
                continue
            else:
                common_difference /= 4
                if (
                    first_term > common_difference
                    and first_term < 4 * common_difference
                ):  # since x,y,z are positive integers
                    frequency[n] += 1  # so z>0 and a>d ,also 4d<a

    UpperCamelCase__							:				Any          =      sum(1 for x in frequency[1:limit] if x == 10      )

    return count


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

