






"""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_       :
	__magic_name__	      =				BlenderbotSmallConfig
	__magic_name__	      =				{}
	__magic_name__	      =				'''gelu'''





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

				UpperCAmelCase_     : List[str]			     =					hidden_dropout_prob
				UpperCAmelCase_     : Optional[Any]			     =					attention_probs_dropout_prob
				UpperCAmelCase_     : Dict			     =					max_position_embeddings
				UpperCAmelCase_     : Tuple			     =					eos_token_id
				UpperCAmelCase_     : Any			     =					pad_token_id
				UpperCAmelCase_     : Dict			     =					bos_token_id





	def      _SCREAMING_SNAKE_CASE      (      self					: List[str]						)						->			str:
				UpperCAmelCase_     : str			     =					ids_tensor([self.batch_size, self.seq_length - 1]	,	self.vocab_size						)
				UpperCAmelCase_     : Optional[int]			     =					tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size						)	,	1						)
				UpperCAmelCase_     : Dict			     =					tf.concat([input_ids, eos_tensor]	,	axis=1						)

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

				UpperCAmelCase_     : Union[str, Any]			     =					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_     : Dict			     =					prepare_blenderbot_small_inputs_dict(lowerCAmelCase_	,	lowerCAmelCase_	,	lowerCAmelCase_						)
				return config, inputs_dict





	def      _SCREAMING_SNAKE_CASE      (      self					: Optional[Any]	,	lowerCAmelCase_					: Optional[Any]	,	lowerCAmelCase_					: Dict						)						->			List[Any]:
				UpperCAmelCase_     : List[Any]			     =					TFBlenderbotSmallModel(config=lowerCAmelCase_						).get_decoder()
				UpperCAmelCase_     : Union[str, Any]			     =					inputs_dict["input_ids"]

				UpperCAmelCase_     : Any			     =					input_ids[:1, :]
				UpperCAmelCase_     : Dict			     =					inputs_dict["attention_mask"][:1, :]
				UpperCAmelCase_     : Optional[int]			     =					inputs_dict["head_mask"]
				UpperCAmelCase_     : Union[str, Any]			     =					1

				# first forward pass
				UpperCAmelCase_     : str			     =					model(lowerCAmelCase_	,	attention_mask=lowerCAmelCase_	,	head_mask=lowerCAmelCase_	,	use_cache=lowerCAmelCase_						)

				UpperCAmelCase_     ,	UpperCAmelCase_     : Tuple			     =					outputs.to_tuple()

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

				# append to next input_ids and
				UpperCAmelCase_     : List[Any]			     =					tf.concat([input_ids, next_tokens]	,	axis=-1						)
				UpperCAmelCase_     : Union[str, Any]			     =					tf.concat([attention_mask, next_attn_mask]	,	axis=-1						)

				UpperCAmelCase_     : Any			     =					model(lowerCAmelCase_	,	attention_mask=lowerCAmelCase_						)[0]
				UpperCAmelCase_     : int			     =					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_     : Tuple			     =					int(ids_tensor((1,)	,	output_from_past.shape[-1]						)						)
				UpperCAmelCase_     : List[Any]			     =					output_from_no_past[:, -3:, random_slice_idx]
				UpperCAmelCase_     : Union[str, Any]			     =					output_from_past[:, :, random_slice_idx]

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





def 				snake_case      (   A__	,A__	,A__	,A__=None	,A__=None	,A__=None	,A__=None	,A__=None	,):
			if attention_mask is None:
						UpperCAmelCase_     : str			     =					tf.cast(tf.math.not_equal(A__	,config.pad_token_id						)	,tf.inta						)
			if decoder_attention_mask is None:
						UpperCAmelCase_     : Optional[int]			     =					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_     : str			     =					tf.ones((config.encoder_layers, config.encoder_attention_heads)						)
			if decoder_head_mask is None:
						UpperCAmelCase_     : str			     =					tf.ones((config.decoder_layers, config.decoder_attention_heads)						)
			if cross_attn_head_mask is None:
						UpperCAmelCase_     : Optional[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 			UpperCamelCase_       (__A				,						__A				,						unittest.TestCase     ):
	__magic_name__	      =				(
	    (TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel) if is_tf_available() else ()
	)
	__magic_name__	      =				(TFBlenderbotSmallForConditionalGeneration,) if is_tf_available() else ()
	__magic_name__	      =				(
	    {
	        '''conversational''': TFBlenderbotSmallForConditionalGeneration,
	        '''feature-extraction''': TFBlenderbotSmallModel,
	        '''summarization''': TFBlenderbotSmallForConditionalGeneration,
	        '''text2text-generation''': TFBlenderbotSmallForConditionalGeneration,
	        '''translation''': TFBlenderbotSmallForConditionalGeneration,
	    }
	    if is_tf_available()
	    else {}
	)
	__magic_name__	      =				True
	__magic_name__	      =				False
	__magic_name__	      =				False





	def      _SCREAMING_SNAKE_CASE      (      self					: List[str]						)						->			List[Any]:
				UpperCAmelCase_     : Optional[int]			     =					TFBlenderbotSmallModelTester(self						)
				UpperCAmelCase_     : str			     =					ConfigTester(self	,	config_class=lowerCAmelCase_						)





	def      _SCREAMING_SNAKE_CASE      (      self					: int						)						->			str:
				self.config_tester.run_common_tests()





	def      _SCREAMING_SNAKE_CASE      (      self					: Any						)						->			Optional[int]:
				UpperCAmelCase_     : List[str]			     =					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     ):
	__magic_name__	      =				[
	    '''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?'''
	]
	__magic_name__	      =				'''facebook/blenderbot_small-90M'''





	@cached_property
	def      _SCREAMING_SNAKE_CASE      (      self					: str						)						->			Optional[Any]:
				# use "old" tokenizer here because of bug when downloading new tokenizer
				return BlenderbotSmallTokenizer.from_pretrained("facebook/blenderbot-90M"						)





	@cached_property
	def      _SCREAMING_SNAKE_CASE      (      self					: List[str]						)						->			Optional[Any]:
				UpperCAmelCase_     : str			     =					TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name						)
				return model





	@slow
	def      _SCREAMING_SNAKE_CASE      (      self					: str						)						->			str:
				UpperCAmelCase_     : Dict			     =					self.tokenizer(self.src_text	,	return_tensors="tf"						)
				UpperCAmelCase_     : List[str]			     =					self.model.generate(
				    model_inputs.input_ids	,	attention_mask=model_inputs.attention_mask	,	num_beams=2	,	use_cache=lowerCAmelCase_	,	)
				UpperCAmelCase_     : Dict			     =					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"""
from torch import nn

def 				snake_case      (   A__						):
			if act_fn in ["swish", "silu"]:
						return nn.SiLU()
			elif act_fn == "mish":
						return nn.Mish()
			elif act_fn == "gelu":
						return nn.GELU()
			else:
						raise ValueError(F"""Unsupported activation function: {act_fn}"""						)



