



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 :
	SCREAMING_SNAKE_CASE       =       BlenderbotConfig
	SCREAMING_SNAKE_CASE       =       {}
	SCREAMING_SNAKE_CASE       =       "gelu"



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

						__lowerCAmelCase	   =hidden_dropout_prob
						__lowerCAmelCase	   =attention_probs_dropout_prob
						__lowerCAmelCase	   =max_position_embeddings
						__lowerCAmelCase	   =eos_token_id
						__lowerCAmelCase	   =pad_token_id
						__lowerCAmelCase	   =bos_token_id



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

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

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



	def 		UpperCamelCase							(						self							:    Optional[int]				,	snake_case_							:    str				,	snake_case_							:    Dict)->							Any:
						__lowerCAmelCase	   =TFBlenderbotModel(config=snake_case_).get_decoder()
						__lowerCAmelCase	   =inputs_dict["""input_ids"""]

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

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

						__lowerCAmelCase   ,	__lowerCAmelCase	   =outputs.to_tuple()

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

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

						__lowerCAmelCase	   =model(snake_case_				,	attention_mask=snake_case_)[0]
						__lowerCAmelCase	   =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
						__lowerCAmelCase	   =int(ids_tensor((1,)				,	output_from_past.shape[-1]))
						__lowerCAmelCase	   =output_from_no_past[:, -3:, random_slice_idx]
						__lowerCAmelCase	   =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 					__lowerCAmelCase		(  __lowerCamelCase	:    str				,   __lowerCamelCase	:    List[Any]				,   __lowerCamelCase	:    List[str]				,   __lowerCamelCase	:    Optional[Any]=None				,   __lowerCamelCase	:    int=None				,   __lowerCamelCase	:    Optional[int]=None				,   __lowerCamelCase	:    int=None				,   __lowerCamelCase	:    Tuple=None				,   )  ->						Optional[Any]:
					if attention_mask is None:
										__lowerCAmelCase	   =tf.cast(tf.math.not_equal(__lowerCamelCase				,   config.pad_token_id	)				,   tf.inta	)
					if decoder_attention_mask is None:
										__lowerCAmelCase	   =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	   =tf.ones((config.encoder_layers, config.encoder_attention_heads)	)
					if decoder_head_mask is None:
										__lowerCAmelCase	   =tf.ones((config.decoder_layers, config.decoder_attention_heads)	)
					if cross_attn_head_mask is None:
										__lowerCAmelCase	   =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				):
	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 		UpperCamelCase							(						self							:    List[Any])->							Union[str, Any]:
						__lowerCAmelCase	   =TFBlenderbotModelTester(self)
						__lowerCAmelCase	   =ConfigTester(self				,	config_class=snake_case_)



	def 		UpperCamelCase							(						self							:    Any)->							List[str]:
						self.config_tester.run_common_tests()



	def 		UpperCamelCase							(						self							:    int)->							List[str]:
						__lowerCAmelCase	   =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				):
	SCREAMING_SNAKE_CASE       =       ["My friends are cool but they eat too many carbs."]
	SCREAMING_SNAKE_CASE       =       "facebook/blenderbot-400M-distill"



	@cached_property
	def 		UpperCamelCase							(						self							:    List[str])->							Optional[int]:
						return BlenderbotTokenizer.from_pretrained(self.model_name)



	@cached_property
	def 		UpperCamelCase							(						self							:    Optional[Any])->							Optional[Any]:
						__lowerCAmelCase	   =TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name)
						return model



	@slow
	def 		UpperCamelCase							(						self							:    str)->							Any:
						__lowerCAmelCase	   =self.tokenizer(self.src_text				,	return_tensors="""tf""")
						__lowerCAmelCase	   =self.model.generate(
						    model_inputs.input_ids				,	)
						__lowerCAmelCase	   =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?"
						)








def 					__lowerCAmelCase		(  __lowerCamelCase	:    int	)  ->						list:
					__lowerCAmelCase	   =int(__lowerCamelCase	)
					if n_element < 1:
										__lowerCAmelCase	   =ValueError("""a should be a positive number"""	)
										raise my_error

					__lowerCAmelCase	   =[1]
					__lowerCAmelCase   ,	__lowerCAmelCase   ,	__lowerCAmelCase	   =(0, 0, 0)
					__lowerCAmelCase	   =1
					while index < n_element:
										while hamming_list[i] * 2 <= hamming_list[-1]:
															i += 1
										while hamming_list[j] * 3 <= hamming_list[-1]:
															j += 1
										while hamming_list[k] * 5 <= hamming_list[-1]:
															k += 1
										hamming_list.append(
										    min(hamming_list[i] * 2				,   hamming_list[j] * 3				,   hamming_list[k] * 5	)	)
										index += 1
					return hamming_list


if __name__ == "__main__":
				lowercase_	       =			input('''Enter the last number (nth term) of the Hamming Number Series: ''')
				print('''Formula of Hamming Number Series => 2^i * 3^j * 5^k''')
				lowercase_	       =			hamming(int(n))
				print('''-----------------------------------------------------''')
				print(F"The list with nth numbers is: {hamming_numbers}")
				print('''-----------------------------------------------------''')




