






'''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__     :




		'''simple docstring'''





		_UpperCamelCase						=       BlenderbotSmallConfig
		_UpperCamelCase						=       {}
		_UpperCamelCase						=       'gelu'






		def __init__(   self ,_lowerCAmelCase ,_lowerCAmelCase=13 ,_lowerCAmelCase=7 ,_lowerCAmelCase=True ,_lowerCAmelCase=False ,_lowerCAmelCase=99 ,_lowerCAmelCase=32 ,_lowerCAmelCase=2 ,_lowerCAmelCase=4 ,_lowerCAmelCase=37 ,_lowerCAmelCase=0.1 ,_lowerCAmelCase=0.1 ,_lowerCAmelCase=20 ,_lowerCAmelCase=2 ,_lowerCAmelCase=1 ,_lowerCAmelCase=0 ,):
								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							):
								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_small_inputs_dict(_UpperCamelCase ,_UpperCamelCase ,_UpperCamelCase							)
								return config, inputs_dict






		def       UpperCamelCase_       (   self ,_lowerCAmelCase ,_lowerCAmelCase							):
								lowerCamelCase__								=	TFBlenderbotSmallModel(config=_UpperCamelCase							).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(_UpperCamelCase ,attention_mask=_UpperCamelCase ,head_mask=_UpperCamelCase ,use_cache=_UpperCamelCase							)

								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(_UpperCamelCase ,attention_mask=_UpperCamelCase							)[0]
								lowerCamelCase__								=	model(_UpperCamelCase ,attention_mask=_UpperCamelCase ,past_key_values=_UpperCamelCase							)[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(_UpperCamelCase ,_UpperCamelCase ,rtol=1E-3							)



def     A__      (					__lowerCAmelCase		:      Tuple ,     __lowerCAmelCase		:      str ,     __lowerCAmelCase		:      Tuple ,     __lowerCAmelCase		:      Union[str, Any]=None ,     __lowerCAmelCase		:      Optional[Any]=None ,     __lowerCAmelCase		:      Optional[int]=None ,     __lowerCAmelCase		:      List[str]=None ,     __lowerCAmelCase		:      int=None ,     ):
						if attention_mask is None:
												lowerCamelCase__								=	tf.cast(tf.math.not_equal(__UpperCamelCase ,     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 		UpperCamelCase__     (a ,a ,unittest.TestCase      ):




		'''simple docstring'''





		_UpperCamelCase						=       (
		    (TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel) if is_tf_available() else ()
		)
		_UpperCamelCase						=       (TFBlenderbotSmallForConditionalGeneration,) if is_tf_available() else ()
		_UpperCamelCase						=       (
		    {
		        'conversational': TFBlenderbotSmallForConditionalGeneration,
		        'feature-extraction': TFBlenderbotSmallModel,
		        'summarization': TFBlenderbotSmallForConditionalGeneration,
		        'text2text-generation': TFBlenderbotSmallForConditionalGeneration,
		        'translation': TFBlenderbotSmallForConditionalGeneration,
		    }
		    if is_tf_available()
		    else {}
		)
		_UpperCamelCase						=       True
		_UpperCamelCase						=       False
		_UpperCamelCase						=       False






		def       UpperCamelCase_       (   self							):
								lowerCamelCase__								=	TFBlenderbotSmallModelTester(self							)
								lowerCamelCase__								=	ConfigTester(self ,config_class=_UpperCamelCase							)






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






		def       UpperCamelCase_       (   self							):
								lowerCamelCase__								=	self.model_tester.prepare_config_and_inputs_for_common()
								self.model_tester.check_decoder_model_past_large_inputs(*_UpperCamelCase							)




@require_tokenizers
@require_tf
class 		UpperCamelCase__     (unittest.TestCase      ):




		'''simple docstring'''





		_UpperCamelCase						=       [
		    '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?'
		]
		_UpperCamelCase						=       'facebook/blenderbot_small-90M'






		@cached_property
		def       UpperCamelCase_       (   self							):
								# use "old" tokenizer here because of bug when downloading new tokenizer
								return BlenderbotSmallTokenizer.from_pretrained("""facebook/blenderbot-90M"""							)






		@cached_property
		def       UpperCamelCase_       (   self							):
								lowerCamelCase__								=	TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name							)
								return model






		@slow
		def       UpperCamelCase_       (   self							):
								lowerCamelCase__								=	self.tokenizer(self.src_text ,return_tensors="""tf"""							)
								lowerCamelCase__								=	self.model.generate(
								    model_inputs.input_ids ,attention_mask=model_inputs.attention_mask ,num_beams=2 ,use_cache=_UpperCamelCase ,)
								lowerCamelCase__								=	self.tokenizer.batch_decode(generated_ids.numpy() ,skip_special_tokens=_UpperCamelCase							)[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 .testing import (
    are_the_same_tensors,
    execute_subprocess_async,
    require_bnb,
    require_cpu,
    require_cuda,
    require_huggingface_suite,
    require_mps,
    require_multi_gpu,
    require_multi_xpu,
    require_safetensors,
    require_single_gpu,
    require_single_xpu,
    require_torch_min_version,
    require_tpu,
    require_xpu,
    skip,
    slow,
)
from .training import RegressionDataset, RegressionModel, RegressionModelaXPU


from .scripts import test_script, test_sync, test_ops  # isort: skip





