





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:
		lowerCamelCase__     :List[Any]   				=		BlenderbotConfig
		lowerCamelCase__     :Optional[Any]   				=		{}
		lowerCamelCase__     :List[Any]   				=		'gelu'







		def __init__(    self ,						__snake_case ,						__snake_case=1_3 ,						__snake_case=7 ,						__snake_case=True ,						__snake_case=False ,						__snake_case=9_9 ,						__snake_case=3_2 ,						__snake_case=2 ,						__snake_case=4 ,						__snake_case=3_7 ,						__snake_case=0.1 ,						__snake_case=0.1 ,						__snake_case=2_0 ,						__snake_case=2 ,						__snake_case=1 ,						__snake_case=0 ,						)      ->      str:


							'''simple docstring'''

							_snake_case       :					Any				       =   parent
							_snake_case       :					Optional[int]				       =   batch_size
							_snake_case       :					List[Any]				       =   seq_length
							_snake_case       :					Optional[int]				       =   is_training
							_snake_case       :					Union[str, Any]				       =   use_labels
							_snake_case       :					List[str]				       =   vocab_size
							_snake_case       :					Dict				       =   hidden_size
							_snake_case       :					Dict				       =   num_hidden_layers
							_snake_case       :					Tuple				       =   num_attention_heads
							_snake_case       :					Tuple				       =   intermediate_size

							_snake_case       :					str				       =   hidden_dropout_prob
							_snake_case       :					str				       =   attention_probs_dropout_prob
							_snake_case       :					Tuple				       =   max_position_embeddings
							_snake_case       :					Any				       =   eos_token_id
							_snake_case       :					Optional[int]				       =   pad_token_id
							_snake_case       :					Optional[Any]				       =   bos_token_id







		def        lowercase       (    self				)      ->      Dict:


							'''simple docstring'''

							_snake_case       :					str				       =   ids_tensor([self.batch_size, self.seq_length - 1] ,						self.vocab_size				)
							_snake_case       :					List[Any]				       =   tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size				) ,						1				)
							_snake_case       :					Union[str, Any]				       =   tf.concat([input_ids, eos_tensor] ,						axis=1				)

							_snake_case       :					str				       =   ids_tensor([self.batch_size, self.seq_length] ,						self.vocab_size				)

							_snake_case       :					List[str]				       =   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       :					List[str]				       =   prepare_blenderbot_inputs_dict(__snake_case ,						__snake_case ,						__snake_case				)
							return config, inputs_dict







		def        lowercase       (    self ,						__snake_case ,						__snake_case				)      ->      Any:


							'''simple docstring'''

							_snake_case       :					Union[str, Any]				       =   TFBlenderbotModel(config=__snake_case				).get_decoder()
							_snake_case       :					Dict				       =   inputs_dict["input_ids"]

							_snake_case       :					Any				       =   input_ids[:1, :]
							_snake_case       :					Union[str, Any]				       =   inputs_dict["attention_mask"][:1, :]
							_snake_case       :					Union[str, Any]				       =   inputs_dict["head_mask"]
							_snake_case       :					List[str]				       =   1

							# first forward pass
							_snake_case       :					Union[str, Any]				       =   model(__snake_case ,						attention_mask=__snake_case ,						head_mask=__snake_case ,						use_cache=__snake_case				)

							_snake_case       , _snake_case       :					Optional[int]				       =   outputs.to_tuple()

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

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

							_snake_case       :					Any				       =   model(__snake_case ,						attention_mask=__snake_case				)[0]
							_snake_case       :					Union[str, Any]				       =   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
							_snake_case       :					str				       =   int(ids_tensor((1,) ,						output_from_past.shape[-1]				)				)
							_snake_case       :					Optional[int]				       =   output_from_no_past[:, -3:, random_slice_idx]
							_snake_case       :					Dict				       =   output_from_past[:, :, random_slice_idx]

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







def    A				(							UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase=None  ,  UpperCAmelCase=None  ,  UpperCAmelCase=None  ,  UpperCAmelCase=None  ,  UpperCAmelCase=None  ,  ):
					if attention_mask is None:
										_snake_case       :					List[str]				       =   tf.cast(tf.math.not_equal(UpperCAmelCase  ,  config.pad_token_id				)  ,  tf.inta				)
					if decoder_attention_mask is None:
										_snake_case       :					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:
										_snake_case       :					Union[str, Any]				       =   tf.ones((config.encoder_layers, config.encoder_attention_heads)				)
					if decoder_head_mask is None:
										_snake_case       :					str				       =   tf.ones((config.decoder_layers, config.decoder_attention_heads)				)
					if cross_attn_head_mask is None:
										_snake_case       :					Any				       =   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    ):
		lowerCamelCase__     :List[str]   				=		(TFBlenderbotForConditionalGeneration, TFBlenderbotModel) if is_tf_available() else ()
		lowerCamelCase__     :List[str]   				=		(TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
		lowerCamelCase__     :List[str]   				=		(
		    {
		        'conversational': TFBlenderbotForConditionalGeneration,
		        'feature-extraction': TFBlenderbotModel,
		        'summarization': TFBlenderbotForConditionalGeneration,
		        'text2text-generation': TFBlenderbotForConditionalGeneration,
		        'translation': TFBlenderbotForConditionalGeneration,
		    }
		    if is_tf_available()
		    else {}
		)
		lowerCamelCase__     :List[str]   				=		True
		lowerCamelCase__     :Optional[int]   				=		False
		lowerCamelCase__     :Optional[int]   				=		False







		def        lowercase       (    self				)      ->      Union[str, Any]:


							'''simple docstring'''

							_snake_case       :					Dict				       =   TFBlenderbotModelTester(self				)
							_snake_case       :					Union[str, Any]				       =   ConfigTester(self ,						config_class=__snake_case				)







		def        lowercase       (    self				)      ->      List[str]:


							'''simple docstring'''

							self.config_tester.run_common_tests()







		def        lowercase       (    self				)      ->      int:


							'''simple docstring'''

							_snake_case       :					Tuple				       =   self.model_tester.prepare_config_and_inputs_for_common()
							self.model_tester.check_decoder_model_past_large_inputs(*__snake_case				)






@require_tokenizers
@require_tf
class        _a(    unittest.TestCase    ):
		lowerCamelCase__     :Tuple   				=		['My friends are cool but they eat too many carbs.']
		lowerCamelCase__     :List[Any]   				=		'facebook/blenderbot-400M-distill'







		@cached_property
		def        lowercase       (    self				)      ->      Optional[int]:


							'''simple docstring'''

							return BlenderbotTokenizer.from_pretrained(self.model_name				)







		@cached_property
		def        lowercase       (    self				)      ->      Union[str, Any]:


							'''simple docstring'''

							_snake_case       :					Dict				       =   TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name				)
							return model







		@slow
		def        lowercase       (    self				)      ->      Optional[int]:


							'''simple docstring'''

							_snake_case       :					int				       =   self.tokenizer(self.src_text ,						return_tensors="tf"				)
							_snake_case       :					Dict				       =   self.model.generate(
							    model_inputs.input_ids ,						)
							_snake_case       :					Dict				       =   self.tokenizer.batch_decode(generated_ids.numpy() ,						skip_special_tokens=__snake_case				)[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				(							UpperCAmelCase  ,  UpperCAmelCase				):
					return (pa[0] + pa[0]) / 2, (pa[1] + pa[1]) / 2







def    A				(							UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  ):
					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(UpperCAmelCase  ,  get_mid(UpperCAmelCase  ,  UpperCAmelCase				)  ,  get_mid(UpperCAmelCase  ,  UpperCAmelCase				)  ,  depth - 1				)
					triangle(UpperCAmelCase  ,  get_mid(UpperCAmelCase  ,  UpperCAmelCase				)  ,  get_mid(UpperCAmelCase  ,  UpperCAmelCase				)  ,  depth - 1				)
					triangle(UpperCAmelCase  ,  get_mid(UpperCAmelCase  ,  UpperCAmelCase				)  ,  get_mid(UpperCAmelCase  ,  UpperCAmelCase				)  ,  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>'
														)
							__lowerCAmelCase :Any     =    turtle.Turtle()
							my_pen.ht()
							my_pen.speed(5)
							my_pen.pencolor('red')

							__lowerCAmelCase :Optional[int]     =    [(-1_75, -1_25), (0, 1_75), (1_75, -1_25)]  # vertices of triangle
							triangle(vertices[0], vertices[1], vertices[2], int(sys.argv[1]))