
'''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 			lowerCAmelCase :
		a							:					Optional[Any]       =					BlenderbotConfig
		a							:					List[Any]       =					{}
		a							:					int       =					'''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					,						):
								_SCREAMING_SNAKE_CASE				= parent
								_SCREAMING_SNAKE_CASE				= batch_size
								_SCREAMING_SNAKE_CASE				= seq_length
								_SCREAMING_SNAKE_CASE				= is_training
								_SCREAMING_SNAKE_CASE				= use_labels
								_SCREAMING_SNAKE_CASE				= vocab_size
								_SCREAMING_SNAKE_CASE				= hidden_size
								_SCREAMING_SNAKE_CASE				= num_hidden_layers
								_SCREAMING_SNAKE_CASE				= num_attention_heads
								_SCREAMING_SNAKE_CASE				= intermediate_size

								_SCREAMING_SNAKE_CASE				= hidden_dropout_prob
								_SCREAMING_SNAKE_CASE				= attention_probs_dropout_prob
								_SCREAMING_SNAKE_CASE				= max_position_embeddings
								_SCREAMING_SNAKE_CASE				= eos_token_id
								_SCREAMING_SNAKE_CASE				= pad_token_id
								_SCREAMING_SNAKE_CASE				= bos_token_id




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

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

								_SCREAMING_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					,						)
								_SCREAMING_SNAKE_CASE				= prepare_blenderbot_inputs_dict(A_					,						A_					,						A_      )
								return config, inputs_dict




		def     lowercase       (				self					,						UpperCamelCase					,						UpperCamelCase      ):
								_SCREAMING_SNAKE_CASE				= TFBlenderbotModel(config=A_      ).get_decoder()
								_SCREAMING_SNAKE_CASE				= inputs_dict["input_ids"]

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

								# first forward pass
								_SCREAMING_SNAKE_CASE				= model(A_					,						attention_mask=A_					,						head_mask=A_					,						use_cache=A_      )

								_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE				= outputs.to_tuple()

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

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

								_SCREAMING_SNAKE_CASE				= model(A_					,						attention_mask=A_      )[0]
								_SCREAMING_SNAKE_CASE				= model(A_					,						attention_mask=A_					,						past_key_values=A_      )[0]

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

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

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



def 			_a		(	_SCREAMING_SNAKE_CASE		:   Union[str, Any]	,			_SCREAMING_SNAKE_CASE		:   Any	,			_SCREAMING_SNAKE_CASE		:   Dict	,			_SCREAMING_SNAKE_CASE		:   Dict=None	,			_SCREAMING_SNAKE_CASE		:   int=None	,			_SCREAMING_SNAKE_CASE		:   Optional[int]=None	,			_SCREAMING_SNAKE_CASE		:   Dict=None	,			_SCREAMING_SNAKE_CASE		:   Dict=None	,			):
						if attention_mask is None:
												_SCREAMING_SNAKE_CASE				= tf.cast(tf.math.not_equal(_SCREAMING_SNAKE_CASE	,			config.pad_token_id  )	,			tf.inta  )
						if decoder_attention_mask is None:
												_SCREAMING_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:
												_SCREAMING_SNAKE_CASE				= tf.ones((config.encoder_layers, config.encoder_attention_heads)  )
						if decoder_head_mask is None:
												_SCREAMING_SNAKE_CASE				= tf.ones((config.decoder_layers, config.decoder_attention_heads)  )
						if cross_attn_head_mask is None:
												_SCREAMING_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 			lowerCAmelCase (				_UpperCAmelCase		,     _UpperCAmelCase		,     unittest.TestCase			):
		a							:					List[str]       =					(TFBlenderbotForConditionalGeneration, TFBlenderbotModel) if is_tf_available() else ()
		a							:					Optional[int]       =					(TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
		a							:					Optional[Any]       =					(
		    {
		        '''conversational''': TFBlenderbotForConditionalGeneration,
		        '''feature-extraction''': TFBlenderbotModel,
		        '''summarization''': TFBlenderbotForConditionalGeneration,
		        '''text2text-generation''': TFBlenderbotForConditionalGeneration,
		        '''translation''': TFBlenderbotForConditionalGeneration,
		    }
		    if is_tf_available()
		    else {}
		)
		a							:					Optional[Any]       =					True
		a							:					Any       =					False
		a							:					Optional[Any]       =					False




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= TFBlenderbotModelTester(self      )
								_SCREAMING_SNAKE_CASE				= ConfigTester(self					,						config_class=A_      )




		def     lowercase       (				self      ):
								self.config_tester.run_common_tests()




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= self.model_tester.prepare_config_and_inputs_for_common()
								self.model_tester.check_decoder_model_past_large_inputs(*A_      )


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




		@cached_property
		def     lowercase       (				self      ):
								return BlenderbotTokenizer.from_pretrained(self.model_name      )




		@cached_property
		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name      )
								return model




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

'''simple docstring'''







def 			_a		(	_SCREAMING_SNAKE_CASE		:   list[int]	,			_SCREAMING_SNAKE_CASE		:   list[int]  ):

						# Check if the input is valid
						if not len(_SCREAMING_SNAKE_CASE  ) == len(_SCREAMING_SNAKE_CASE  ) == 3:
												raise ValueError("Please enter a valid equation."  )
						if equationa[0] == equationa[1] == equationa[0] == equationa[1] == 0:
												raise ValueError("Both a & b of two equations can't be zero."  )

						# Extract the coefficients
						_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE				= equationa
						_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE				= equationa

						# Calculate the determinants of the matrices
						_SCREAMING_SNAKE_CASE				= aa * ba - aa * ba
						_SCREAMING_SNAKE_CASE				= ca * ba - ca * ba
						_SCREAMING_SNAKE_CASE				= aa * ca - aa * ca

						# Check if the system of linear equations has a solution (using Cramer's rule)
						if determinant == 0:
												if determinant_x == determinant_y == 0:
																		raise ValueError("Infinite solutions. (Consistent system)"  )
												else:
																		raise ValueError("No solution. (Inconsistent system)"  )
						else:
												if determinant_x == determinant_y == 0:
																		# Trivial solution (Inconsistent system)
																		return (0.0, 0.0)
												else:
																		_SCREAMING_SNAKE_CASE				= determinant_x / determinant
																		_SCREAMING_SNAKE_CASE				= determinant_y / determinant
																		# Non-Trivial Solution (Consistent system)
																		return (x, y)