





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 SCREAMING_SNAKE_CASE_  :
						__magic_name__:	Dict       					=				BlenderbotSmallConfig
						__magic_name__:	Tuple       					=				{}
						__magic_name__:	str       					=				'''gelu'''






						def __init__(					self					:					Dict						,      _A					:					str						,      _A					:					List[Any]=13						,      _A					:					Optional[int]=7						,      _A					:					Optional[Any]=True						,      _A					:					List[Any]=False						,      _A					:					Dict=99						,      _A					:					Optional[int]=32						,      _A					:					Tuple=2						,      _A					:					str=4						,      _A					:					int=37						,      _A					:					Tuple=0.1						,      _A					:					Tuple=0.1						,      _A					:					Optional[int]=20						,      _A					:					List[str]=2						,      _A					:					Optional[Any]=1						,      _A					:					List[Any]=0						,      )							->			List[Any]:







							"""simple docstring"""


							snake_case_						:	str      =		parent
							snake_case_						:	int      =		batch_size
							snake_case_						:	Union[str, Any]      =		seq_length
							snake_case_						:	List[Any]      =		is_training
							snake_case_						:	List[str]      =		use_labels
							snake_case_						:	Union[str, Any]      =		vocab_size
							snake_case_						:	Optional[Any]      =		hidden_size
							snake_case_						:	str      =		num_hidden_layers
							snake_case_						:	List[Any]      =		num_attention_heads
							snake_case_						:	Any      =		intermediate_size

							snake_case_						:	Optional[int]      =		hidden_dropout_prob
							snake_case_						:	List[str]      =		attention_probs_dropout_prob
							snake_case_						:	Optional[Any]      =		max_position_embeddings
							snake_case_						:	Any      =		eos_token_id
							snake_case_						:	Optional[Any]      =		pad_token_id
							snake_case_						:	List[Any]      =		bos_token_id






						def UpperCAmelCase_ (					self					:					Union[str, Any]		)							->			List[str]:







							"""simple docstring"""


							snake_case_						:	Optional[int]      =		ids_tensor([self.batch_size, self.seq_length - 1]						,      self.vocab_size		)
							snake_case_						:	Optional[int]      =		tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size		)						,      1		)
							snake_case_						:	Union[str, Any]      =		tf.concat([input_ids, eos_tensor]						,      axis=1		)

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

							snake_case_						:	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						,      )
							snake_case_						:	Union[str, Any]      =		prepare_blenderbot_small_inputs_dict(__SCREAMING_SNAKE_CASE						,      __SCREAMING_SNAKE_CASE						,      __SCREAMING_SNAKE_CASE		)
							return config, inputs_dict






						def UpperCAmelCase_ (					self					:					Any						,      _A					:					str						,      _A					:					Any		)							->			Dict:







							"""simple docstring"""


							snake_case_						:	Optional[Any]      =		TFBlenderbotSmallModel(config=__SCREAMING_SNAKE_CASE		).get_decoder()
							snake_case_						:	Optional[int]      =		inputs_dict['input_ids']

							snake_case_						:	Tuple      =		input_ids[:1, :]
							snake_case_						:	Optional[Any]      =		inputs_dict['attention_mask'][:1, :]
							snake_case_						:	Any      =		inputs_dict['head_mask']
							snake_case_						:	int      =		1

							# first forward pass
							snake_case_						:	Optional[Any]      =		model(__SCREAMING_SNAKE_CASE						,      attention_mask=__SCREAMING_SNAKE_CASE						,      head_mask=__SCREAMING_SNAKE_CASE						,      use_cache=__SCREAMING_SNAKE_CASE		)

							snake_case_  ,snake_case_						:	List[Any]      =		outputs.to_tuple()

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

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

							snake_case_						:	int      =		model(__SCREAMING_SNAKE_CASE						,      attention_mask=__SCREAMING_SNAKE_CASE		)[0]
							snake_case_						:	Any      =		model(__SCREAMING_SNAKE_CASE						,      attention_mask=__SCREAMING_SNAKE_CASE						,      past_key_values=__SCREAMING_SNAKE_CASE		)[0]

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

							# select random slice
							snake_case_						:	Optional[int]      =		int(ids_tensor((1,)						,      output_from_past.shape[-1]		)		)
							snake_case_						:	Union[str, Any]      =		output_from_no_past[:, -3:, random_slice_idx]
							snake_case_						:	Optional[int]      =		output_from_past[:, :, random_slice_idx]

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


def 							SCREAMING_SNAKE_CASE__  (			__a      ,       __a      ,       __a      ,       __a=None      ,       __a=None      ,       __a=None      ,       __a=None      ,       __a=None      ,       ):
	if attention_mask is None:
		snake_case_						:	List[Any]      =		tf.cast(tf.math.not_equal(_UpperCAmelCase      ,       config.pad_token_id      )      ,       tf.inta      )
	if decoder_attention_mask is None:
		snake_case_						:	List[Any]      =		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:
		snake_case_						:	List[Any]      =		tf.ones((config.encoder_layers, config.encoder_attention_heads)      )
	if decoder_head_mask is None:
		snake_case_						:	Union[str, Any]      =		tf.ones((config.decoder_layers, config.decoder_attention_heads)      )
	if cross_attn_head_mask is None:
		snake_case_						:	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 SCREAMING_SNAKE_CASE_  (    __UpperCAmelCase				, __UpperCAmelCase				, unittest.TestCase    ):
						__magic_name__:	Union[str, Any]       					=				(
						    (TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel) if is_tf_available() else ()
						)
						__magic_name__:	Union[str, Any]       					=				(TFBlenderbotSmallForConditionalGeneration,) if is_tf_available() else ()
						__magic_name__:	str       					=				(
						    {
						        '''conversational''': TFBlenderbotSmallForConditionalGeneration,
						        '''feature-extraction''': TFBlenderbotSmallModel,
						        '''summarization''': TFBlenderbotSmallForConditionalGeneration,
						        '''text2text-generation''': TFBlenderbotSmallForConditionalGeneration,
						        '''translation''': TFBlenderbotSmallForConditionalGeneration,
						    }
						    if is_tf_available()
						    else {}
						)
						__magic_name__:	Optional[int]       					=				True
						__magic_name__:	int       					=				False
						__magic_name__:	str       					=				False






						def UpperCAmelCase_ (					self					:					int		)							->			Union[str, Any]:







							"""simple docstring"""


							snake_case_						:	Any      =		TFBlenderbotSmallModelTester(self		)
							snake_case_						:	Dict      =		ConfigTester(self						,      config_class=__SCREAMING_SNAKE_CASE		)






						def UpperCAmelCase_ (					self					:					str		)							->			List[Any]:







							"""simple docstring"""


							self.config_tester.run_common_tests()






						def UpperCAmelCase_ (					self					:					List[str]		)							->			List[str]:







							"""simple docstring"""


							snake_case_						:	Optional[Any]      =		self.model_tester.prepare_config_and_inputs_for_common()
							self.model_tester.check_decoder_model_past_large_inputs(*__SCREAMING_SNAKE_CASE		)
@require_tokenizers
@require_tf
class SCREAMING_SNAKE_CASE_  (    unittest.TestCase    ):
						__magic_name__:	Union[str, Any]       					=				[
						    '''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__:	str       					=				'''facebook/blenderbot_small-90M'''






						@cached_property
						def UpperCAmelCase_ (					self					:					Optional[Any]		)							->			Tuple:







							"""simple docstring"""


							return BlenderbotSmallTokenizer.from_pretrained('facebook/blenderbot-90M'		)






						@cached_property
						def UpperCAmelCase_ (					self					:					Optional[int]		)							->			Union[str, Any]:







							"""simple docstring"""


							snake_case_						:	Optional[int]      =		TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name		)
							return model






						@slow
						def UpperCAmelCase_ (					self					:					List[Any]		)							->			List[str]:







							"""simple docstring"""


							snake_case_						:	int      =		self.tokenizer(self.src_text						,      return_tensors='tf'		)
							snake_case_						:	Any      =		self.model.generate(
							    model_inputs.input_ids						,      attention_mask=model_inputs.attention_mask						,      num_beams=2						,      use_cache=__SCREAMING_SNAKE_CASE						,      )
							snake_case_						:	Dict      =		self.tokenizer.batch_decode(generated_ids.numpy()						,      skip_special_tokens=__SCREAMING_SNAKE_CASE		)[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.",
							)






import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest

class     _A       (	__UpperCAmelCase    ):
       UpperCamelCase__  :    Tuple											=						(DDPMParallelScheduler,)



       def 		_lowerCamelCase			(		self							:     int     ,		**__SCREAMING_SNAKE_CASE							:     List[Any]):


           '''simple docstring'''

           __a						       =				{
               '''num_train_timesteps''': 1_000,
               '''beta_start''': 0.00_01,
               '''beta_end''': 0.02,
               '''beta_schedule''': '''linear''',
               '''variance_type''': '''fixed_small''',
               '''clip_sample''': True,
           }

           config.update(**__SCREAMING_SNAKE_CASE)
           return config



       def 		_lowerCamelCase			(		self							:     List[str]):


           '''simple docstring'''

           for timesteps in [1, 5, 100, 1_000]:
               self.check_over_configs(num_train_timesteps=__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase			(		self							:     Tuple):


           '''simple docstring'''

           for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]     ,		[0.0_02, 0.02, 0.2, 2]):
               self.check_over_configs(beta_start=__SCREAMING_SNAKE_CASE     ,		beta_end=__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase			(		self							:     List[str]):


           '''simple docstring'''

           for schedule in ["linear", "squaredcos_cap_v2"]:
               self.check_over_configs(beta_schedule=__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase			(		self							:     Any):


           '''simple docstring'''

           for variance in ["fixed_small", "fixed_large", "other"]:
               self.check_over_configs(variance_type=__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase			(		self							:     List[str]):


           '''simple docstring'''

           for clip_sample in [True, False]:
               self.check_over_configs(clip_sample=__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase			(		self							:     str):


           '''simple docstring'''

           self.check_over_configs(thresholding=__SCREAMING_SNAKE_CASE)
           for threshold in [0.5, 1.0, 2.0]:
               for prediction_type in ["epsilon", "sample", "v_prediction"]:
                   self.check_over_configs(
                       thresholding=__SCREAMING_SNAKE_CASE     ,		prediction_type=__SCREAMING_SNAKE_CASE     ,		sample_max_value=__SCREAMING_SNAKE_CASE     ,		)



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


           '''simple docstring'''

           for prediction_type in ["epsilon", "sample", "v_prediction"]:
               self.check_over_configs(prediction_type=__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase			(		self							:     List[str]):


           '''simple docstring'''

           for t in [0, 500, 999]:
               self.check_over_forward(time_step=__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase			(		self							:     str):


           '''simple docstring'''

           __a						       =				self.scheduler_classes[0]
           __a						       =				self.get_scheduler_config()
           __a						       =				scheduler_class(**__SCREAMING_SNAKE_CASE)

           assert torch.sum(torch.abs(scheduler._get_variance(0) - 0.0)) < 1E-5
           assert torch.sum(torch.abs(scheduler._get_variance(487) - 0.0_09_79)) < 1E-5
           assert torch.sum(torch.abs(scheduler._get_variance(999) - 0.02)) < 1E-5



       def 		_lowerCamelCase			(		self							:     Optional[Any]):


           '''simple docstring'''

           __a						       =				self.scheduler_classes[0]
           __a						       =				self.get_scheduler_config()
           __a						       =				scheduler_class(**__SCREAMING_SNAKE_CASE)

           __a						       =				len(__SCREAMING_SNAKE_CASE)

           __a						       =				self.dummy_model()
           __a						       =				self.dummy_sample_deter
           __a						       =				self.dummy_sample_deter + 0.1
           __a						       =				self.dummy_sample_deter - 0.1

           __a						       =				samplea.shape[0]
           __a						       =				torch.stack([samplea, samplea, samplea]     ,		dim=0)
           __a						       =				torch.arange(__SCREAMING_SNAKE_CASE)[0:3, None].repeat(1     ,		__SCREAMING_SNAKE_CASE)

           __a						       =				model(samples.flatten(0     ,		1)     ,		timesteps.flatten(0     ,		1))
           __a						       =				scheduler.batch_step_no_noise(__SCREAMING_SNAKE_CASE     ,		timesteps.flatten(0     ,		1)     ,		samples.flatten(0     ,		1))

           __a						       =				torch.sum(torch.abs(__SCREAMING_SNAKE_CASE))
           __a						       =				torch.mean(torch.abs(__SCREAMING_SNAKE_CASE))

           assert abs(result_sum.item() - 11_53.18_33) < 1E-2
           assert abs(result_mean.item() - 0.50_05) < 1E-3



       def 		_lowerCamelCase			(		self							:     Dict):


           '''simple docstring'''

           __a						       =				self.scheduler_classes[0]
           __a						       =				self.get_scheduler_config()
           __a						       =				scheduler_class(**__SCREAMING_SNAKE_CASE)

           __a						       =				len(__SCREAMING_SNAKE_CASE)

           __a						       =				self.dummy_model()
           __a						       =				self.dummy_sample_deter
           __a						       =				torch.manual_seed(0)

           for t in reversed(range(__SCREAMING_SNAKE_CASE)):
               # 1. predict noise residual
               __a						       =				model(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)

               # 2. predict previous mean of sample x_t-1
               __a						       =				scheduler.step(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		generator=__SCREAMING_SNAKE_CASE).prev_sample

               __a						       =				pred_prev_sample

           __a						       =				torch.sum(torch.abs(__SCREAMING_SNAKE_CASE))
           __a						       =				torch.mean(torch.abs(__SCREAMING_SNAKE_CASE))

           assert abs(result_sum.item() - 2_58.96_06) < 1E-2
           assert abs(result_mean.item() - 0.33_72) < 1E-3



       def 		_lowerCamelCase			(		self							:     Optional[int]):


           '''simple docstring'''

           __a						       =				self.scheduler_classes[0]
           __a						       =				self.get_scheduler_config(prediction_type='''v_prediction''')
           __a						       =				scheduler_class(**__SCREAMING_SNAKE_CASE)

           __a						       =				len(__SCREAMING_SNAKE_CASE)

           __a						       =				self.dummy_model()
           __a						       =				self.dummy_sample_deter
           __a						       =				torch.manual_seed(0)

           for t in reversed(range(__SCREAMING_SNAKE_CASE)):
               # 1. predict noise residual
               __a						       =				model(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)

               # 2. predict previous mean of sample x_t-1
               __a						       =				scheduler.step(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		generator=__SCREAMING_SNAKE_CASE).prev_sample

               __a						       =				pred_prev_sample

           __a						       =				torch.sum(torch.abs(__SCREAMING_SNAKE_CASE))
           __a						       =				torch.mean(torch.abs(__SCREAMING_SNAKE_CASE))

           assert abs(result_sum.item() - 2_02.02_96) < 1E-2
           assert abs(result_mean.item() - 0.26_31) < 1E-3



       def 		_lowerCamelCase			(		self							:     Optional[int]):


           '''simple docstring'''

           __a						       =				self.scheduler_classes[0]
           __a						       =				self.get_scheduler_config()
           __a						       =				scheduler_class(**__SCREAMING_SNAKE_CASE)

           __a						       =				[100, 87, 50, 1, 0]

           scheduler.set_timesteps(timesteps=__SCREAMING_SNAKE_CASE)

           __a						       =				scheduler.timesteps

           for i, timestep in enumerate(__SCREAMING_SNAKE_CASE):
               if i == len(__SCREAMING_SNAKE_CASE) - 1:
                   __a						       =				-1
               else:
                   __a						       =				timesteps[i + 1]

               __a						       =				scheduler.previous_timestep(__SCREAMING_SNAKE_CASE)
               __a						       =				prev_t.item()

               self.assertEqual(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase			(		self							:     Dict):


           '''simple docstring'''

           __a						       =				self.scheduler_classes[0]
           __a						       =				self.get_scheduler_config()
           __a						       =				scheduler_class(**__SCREAMING_SNAKE_CASE)

           __a						       =				[100, 87, 50, 51, 0]

           with self.assertRaises(__SCREAMING_SNAKE_CASE     ,		msg='''`custom_timesteps` must be in descending order.'''):
               scheduler.set_timesteps(timesteps=__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase			(		self							:     Optional[int]):


           '''simple docstring'''

           __a						       =				self.scheduler_classes[0]
           __a						       =				self.get_scheduler_config()
           __a						       =				scheduler_class(**__SCREAMING_SNAKE_CASE)

           __a						       =				[100, 87, 50, 1, 0]
           __a						       =				len(__SCREAMING_SNAKE_CASE)

           with self.assertRaises(__SCREAMING_SNAKE_CASE     ,		msg='''Can only pass one of `num_inference_steps` or `custom_timesteps`.'''):
               scheduler.set_timesteps(num_inference_steps=__SCREAMING_SNAKE_CASE     ,		timesteps=__SCREAMING_SNAKE_CASE)





       def 		_lowerCamelCase			(		self							:     List[str]):


           '''simple docstring'''

           __a						       =				self.scheduler_classes[0]
           __a						       =				self.get_scheduler_config()
           __a						       =				scheduler_class(**__SCREAMING_SNAKE_CASE)

           __a						       =				[scheduler.config.num_train_timesteps]

           with self.assertRaises(
               __SCREAMING_SNAKE_CASE     ,		msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'''     ,		):
               scheduler.set_timesteps(timesteps=__SCREAMING_SNAKE_CASE)





