

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      A       :
      _snake_case							     =BlenderbotConfig
      _snake_case							     ={}
      _snake_case							     ="""gelu"""



      def __init__(	self:						List[str]		,  _lowerCAmelCase:						Optional[Any]		,  _lowerCAmelCase:						Optional[int]=13		,  _lowerCAmelCase:						List[Any]=7		,  _lowerCAmelCase:						Optional[int]=True		,  _lowerCAmelCase:						Union[str, Any]=False		,  _lowerCAmelCase:						List[str]=99		,  _lowerCAmelCase:						int=32		,  _lowerCAmelCase:						Union[str, Any]=2		,  _lowerCAmelCase:						Optional[int]=4		,  _lowerCAmelCase:						Dict=37		,  _lowerCAmelCase:						Optional[int]=0.1		,  _lowerCAmelCase:						Any=0.1		,  _lowerCAmelCase:						List[str]=20		,  _lowerCAmelCase:						Any=2		,  _lowerCAmelCase:						Tuple=1		,  _lowerCAmelCase:						Tuple=0		,  )						->    Dict:
         '''simple docstring'''

         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_dropout_prob
         UpperCAmelCase_											=attention_probs_dropout_prob
         UpperCAmelCase_											=max_position_embeddings
         UpperCAmelCase_											=eos_token_id
         UpperCAmelCase_											=pad_token_id
         UpperCAmelCase_											=bos_token_id



      def 	lowerCAmelCase__    (	self:						Optional[int]       )						->    Any:
         '''simple docstring'''

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

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






      def 	lowerCAmelCase__    (	self:						Tuple		,  _lowerCAmelCase:						int		,  _lowerCAmelCase:						Tuple       )						->    Tuple:
         '''simple docstring'''

         UpperCAmelCase_											=TFBlenderbotModel(config=lowerCAmelCase__       ).get_decoder()
         UpperCAmelCase_											=inputs_dict["input_ids"]

         UpperCAmelCase_											=input_ids[:1, :]
         UpperCAmelCase_											=inputs_dict["attention_mask"][:1, :]
         UpperCAmelCase_											=inputs_dict["head_mask"]
         UpperCAmelCase_											=1

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

         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(lowerCAmelCase__		,  attention_mask=lowerCAmelCase__       )[0]
         UpperCAmelCase_											=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
         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(lowerCAmelCase__		,  lowerCAmelCase__		,  rtol=1e-3       )




def     a__ (					lowercase__  ,    lowercase__  ,    lowercase__  ,    lowercase__=None  ,    lowercase__=None  ,    lowercase__=None  ,    lowercase__=None  ,    lowercase__=None  ,    ):






   '''simple docstring'''






   if attention_mask is None:
      UpperCAmelCase_											=tf.cast(tf.math.not_equal(_lowercase  ,    config.pad_token_id  )  ,    tf.inta  )
   if decoder_attention_mask is None:
      UpperCAmelCase_											=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:
      UpperCAmelCase_											=tf.ones((config.encoder_layers, config.encoder_attention_heads)  )
   if decoder_head_mask is None:
      UpperCAmelCase_											=tf.ones((config.decoder_layers, config.decoder_attention_heads)  )
   if cross_attn_head_mask is None:
      UpperCAmelCase_											=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       (	a__		,      a__		,      unittest.TestCase						):
      _snake_case							     =(TFBlenderbotForConditionalGeneration, TFBlenderbotModel) if is_tf_available() else ()
      _snake_case							     =(TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
      _snake_case							     =(
          {
              """conversational""": TFBlenderbotForConditionalGeneration,
              """feature-extraction""": TFBlenderbotModel,
              """summarization""": TFBlenderbotForConditionalGeneration,
              """text2text-generation""": TFBlenderbotForConditionalGeneration,
              """translation""": TFBlenderbotForConditionalGeneration,
          }
          if is_tf_available()
          else {}
      )
      _snake_case							     =True
      _snake_case							     =False
      _snake_case							     =False



      def 	lowerCAmelCase__    (	self:						Optional[Any]       )						->    Optional[Any]:
         '''simple docstring'''

         UpperCAmelCase_											=TFBlenderbotModelTester(self       )
         UpperCAmelCase_											=ConfigTester(self		,  config_class=lowerCAmelCase__       )



      def 	lowerCAmelCase__    (	self:						List[str]       )						->    List[str]:
         '''simple docstring'''

         self.config_tester.run_common_tests()






      def 	lowerCAmelCase__    (	self:						Any       )						->    int:
         '''simple docstring'''

         UpperCAmelCase_											=self.model_tester.prepare_config_and_inputs_for_common()
         self.model_tester.check_decoder_model_past_large_inputs(*lowerCAmelCase__       )

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



      @cached_property
      def 	lowerCAmelCase__    (	self:						List[str]       )						->    Any:
         '''simple docstring'''

         return BlenderbotTokenizer.from_pretrained(self.model_name       )



      @cached_property
      def 	lowerCAmelCase__    (	self:						List[str]       )						->    List[Any]:
         '''simple docstring'''

         UpperCAmelCase_											=TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name       )
         return model






      @slow
      def 	lowerCAmelCase__    (	self:						Dict       )						->    Union[str, Any]:
         '''simple docstring'''

         UpperCAmelCase_											=self.tokenizer(self.src_text		,  return_tensors="tf"       )
         UpperCAmelCase_											=self.model.generate(
             model_inputs.input_ids		,  )
         UpperCAmelCase_											=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?"
         )











import sys
import turtle




def     a__ (					lowercase__  ,    lowercase__  ):






   '''simple docstring'''






   return (pa[0] + pa[0]) / 2, (pa[1] + pa[1]) / 2




def     a__ (					lowercase__  ,    lowercase__  ,    lowercase__  ,    lowercase__  ,    ):






   '''simple docstring'''






   my_pen.up()
   my_pen.goto(vertexa[0]  ,    vertexa[1]  )
   my_pen.down()
   my_pen.goto(vertexa[0]  ,    vertexa[1]  )
   my_pen.goto(vertexa[0]  ,    vertexa[1]  )
   my_pen.goto(vertexa[0]  ,    vertexa[1]  )

   if depth == 0:
      return

   triangle(lowercase__  ,    get_mid(lowercase__  ,    lowercase__  )  ,    get_mid(lowercase__  ,    lowercase__  )  ,    depth - 1  )
   triangle(lowercase__  ,    get_mid(lowercase__  ,    lowercase__  )  ,    get_mid(lowercase__  ,    lowercase__  )  ,    depth - 1  )
   triangle(lowercase__  ,    get_mid(lowercase__  ,    lowercase__  )  ,    get_mid(lowercase__  ,    lowercase__  )  ,    depth - 1  )


if __name__ == "__main__":
     if len(sys.argv) != 2:
          raise ValueError(
              """Correct format for using this script: """
              """python fractals.py <int:depth_for_fractal>"""
          )
     __lowercase  :   Any						      =turtle.Turtle()
     my_pen.ht()
     my_pen.speed(5)
     my_pen.pencolor("""red""")

     __lowercase  :   str						      =[(-175, -125), (0, 175), (175, -125)]  # vertices of triangle
     triangle(vertices[0], vertices[1], vertices[2], int(sys.argv[1]))




