





'''simple docstring'''


from __future__ import annotations

import unittest

from transformers import BlenderbotSmallConfig, BlenderbotSmallTokenizer, 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, TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel






@require_tf
class       UpperCamelCase__  :
	lowerCAmelCase__					:			Union[str, Any]								=						BlenderbotSmallConfig
	lowerCAmelCase__					:			Optional[Any]								=						{}
	lowerCAmelCase__					:			Tuple								=						"gelu"







	def __init__(				self			:   Optional[int] , lowerCamelCase			:   str , lowerCamelCase			:   Dict=1_3 , lowerCamelCase			:   Optional[Any]=7 , lowerCamelCase			:   Optional[Any]=True , lowerCamelCase			:   List[Any]=False , lowerCamelCase			:   int=9_9 , lowerCamelCase			:   Tuple=3_2 , lowerCamelCase			:   Tuple=2 , lowerCamelCase			:   int=4 , lowerCamelCase			:   Tuple=3_7 , lowerCamelCase			:   Tuple=0.1 , lowerCamelCase			:   List[str]=0.1 , lowerCamelCase			:   str=2_0 , lowerCamelCase			:   Tuple=2 , lowerCamelCase			:   Tuple=1 , lowerCamelCase			:   str=0 , ):




				'''simple docstring'''
				a__     = parent
				a__     = batch_size
				a__     = seq_length
				a__     = is_training
				a__     = use_labels
				a__     = vocab_size
				a__     = hidden_size
				a__     = num_hidden_layers
				a__     = num_attention_heads
				a__     = intermediate_size

				a__     = hidden_dropout_prob
				a__     = attention_probs_dropout_prob
				a__     = max_position_embeddings
				a__     = eos_token_id
				a__     = pad_token_id
				a__     = bos_token_id







	def 						__a   (				self			:   Tuple		):




				'''simple docstring'''
				a__     = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size		)
				a__     = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size		) , 1		)
				a__     = tf.concat([input_ids, eos_tensor] , axis=1		)

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

				a__     = 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 , )
				a__     = prepare_blenderbot_small_inputs_dict(lowerCamelCase , lowerCamelCase , lowerCamelCase		)
				return config, inputs_dict







	def 						__a   (				self			:   Union[str, Any] , lowerCamelCase			:   Union[str, Any] , lowerCamelCase			:   List[Any]		):




				'''simple docstring'''
				a__     = TFBlenderbotSmallModel(config=lowerCamelCase		).get_decoder()
				a__     = inputs_dict["input_ids"]

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

				# first forward pass
				a__     = model(lowerCamelCase , attention_mask=lowerCamelCase , head_mask=lowerCamelCase , use_cache=lowerCamelCase		)

				a__   ,				a__     = outputs.to_tuple()

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

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

				a__     = model(lowerCamelCase , attention_mask=lowerCamelCase		)[0]
				a__     = 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
				a__     = int(ids_tensor((1,) , output_from_past.shape[-1]		)		)
				a__     = output_from_no_past[:, -3:, random_slice_idx]
				a__     = output_from_past[:, :, random_slice_idx]

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







def    _lowerCamelCase  (__lowerCamelCase  :     Any					,  __lowerCamelCase  :     List[str]					,  __lowerCamelCase  :     Dict					,  __lowerCamelCase  :     List[Any]=None					,  __lowerCamelCase  :     Union[str, Any]=None					,  __lowerCamelCase  :     Optional[Any]=None					,  __lowerCamelCase  :     List[str]=None					,  __lowerCamelCase  :     Tuple=None					,  )   ->    Optional[int]:
			if attention_mask is None:
						a__     = tf.cast(tf.math.not_equal(__lowerCamelCase					,  config.pad_token_id     )					,  tf.inta     )
			if decoder_attention_mask is None:
						a__     = 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:
						a__     = tf.ones((config.encoder_layers, config.encoder_attention_heads)     )
			if decoder_head_mask is None:
						a__     = tf.ones((config.decoder_layers, config.decoder_attention_heads)     )
			if cross_attn_head_mask is None:
						a__     = 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     ):
	lowerCAmelCase__					:			Dict								=						(
	    (TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel) if is_tf_available() else ()
	)
	lowerCAmelCase__					:			Optional[int]								=						(TFBlenderbotSmallForConditionalGeneration,) if is_tf_available() else ()
	lowerCAmelCase__					:			Optional[Any]								=						(
	    {
	        "conversational": TFBlenderbotSmallForConditionalGeneration,
	        "feature-extraction": TFBlenderbotSmallModel,
	        "summarization": TFBlenderbotSmallForConditionalGeneration,
	        "text2text-generation": TFBlenderbotSmallForConditionalGeneration,
	        "translation": TFBlenderbotSmallForConditionalGeneration,
	    }
	    if is_tf_available()
	    else {}
	)
	lowerCAmelCase__					:			List[str]								=						True
	lowerCAmelCase__					:			Tuple								=						False
	lowerCAmelCase__					:			int								=						False







	def 						__a   (				self			:   Dict		):




				'''simple docstring'''
				a__     = TFBlenderbotSmallModelTester(self		)
				a__     = ConfigTester(self , config_class=lowerCamelCase		)







	def 						__a   (				self			:   int		):




				'''simple docstring'''
				self.config_tester.run_common_tests()







	def 						__a   (				self			:   Union[str, Any]		):




				'''simple docstring'''
				a__     = 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     ):
	lowerCAmelCase__					:			List[str]								=						[
	    "Social anxiety\nWow, I am never shy. Do you have anxiety?\nYes. I end up sweating and blushing and feel like "
	    "  i'm going to throw up.\nand why is that?"
	]
	lowerCAmelCase__					:			int								=						"facebook/blenderbot_small-90M"







	@cached_property
	def 						__a   (				self			:   str		):




				'''simple docstring'''
				# use "old" tokenizer here because of bug when downloading new tokenizer
				return BlenderbotSmallTokenizer.from_pretrained("facebook/blenderbot-90M"		)







	@cached_property
	def 						__a   (				self			:   Any		):




				'''simple docstring'''
				a__     = TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name		)
				return model







	@slow
	def 						__a   (				self			:   Dict		):




				'''simple docstring'''
				a__     = self.tokenizer(self.src_text , return_tensors="tf"		)
				a__     = self.model.generate(
				    model_inputs.input_ids , attention_mask=model_inputs.attention_mask , num_beams=2 , use_cache=lowerCamelCase , )
				a__     = self.tokenizer.batch_decode(generated_ids.numpy() , skip_special_tokens=lowerCamelCase		)[0]
				assert generated_words in (
				    "i don't know. i just feel like i'm going to throw up. it's not fun.",
				    "i'm not sure. i just feel like i've been feeling like i have to be in a certain place",
				    "i'm not sure. i just feel like i've been in a bad situation.",
				)










'''simple docstring'''


def    _lowerCamelCase  (__lowerCamelCase  :     str     )   ->    bool:
			a__     = 0
			for ch in input_str:
						a__     = ord(__lowerCamelCase     )
						a__     = pow(2					,  __lowerCamelCase     )

						# If we already turned on bit for current character's unicode
						if bitmap >> ch_unicode & 1 == 1:
									return False
						bitmap |= ch_bit_index_on
			return True


if __name__ == "__main__":
		import doctest

		doctest.testmod()



