




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 							__UpperCamelCase			:





				'''simple docstring'''



				__a							: Dict										=BlenderbotConfig
				__a							: Dict										={}
				__a							: Dict										="""gelu"""



				def __init__( self       , UpperCAmelCase_       , UpperCAmelCase_=13       , UpperCAmelCase_=7       , UpperCAmelCase_=True       , UpperCAmelCase_=False       , UpperCAmelCase_=99       , UpperCAmelCase_=32       , UpperCAmelCase_=2       , UpperCAmelCase_=4       , UpperCAmelCase_=37       , UpperCAmelCase_=0.1       , UpperCAmelCase_=0.1       , UpperCAmelCase_=20       , UpperCAmelCase_=2       , UpperCAmelCase_=1       , UpperCAmelCase_=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       __snake_case		( 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_inputs_dict(UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_						)
											return config, inputs_dict






				def       __snake_case		( self       , UpperCAmelCase_       , UpperCAmelCase_						):
											lowerCAmelCase   =		TFBlenderbotModel(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       UpperCAmelCase       (     _snake_case						, _snake_case						, _snake_case						, _snake_case=None						, _snake_case=None						, _snake_case=None						, _snake_case=None						, _snake_case=None						, ):
							if attention_mask is None:
														lowerCAmelCase   =		tf.cast(tf.math.not_equal(_snake_case						, 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			( __UpperCAmelCase     ,					__UpperCAmelCase     ,					unittest.TestCase  ):





				'''simple docstring'''



				__a							: Union[str, Any]										=(TFBlenderbotForConditionalGeneration, TFBlenderbotModel) if is_tf_available() else ()
				__a							: Union[str, Any]										=(TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
				__a							: int										=(
				    {
				        """conversational""": TFBlenderbotForConditionalGeneration,
				        """feature-extraction""": TFBlenderbotModel,
				        """summarization""": TFBlenderbotForConditionalGeneration,
				        """text2text-generation""": TFBlenderbotForConditionalGeneration,
				        """translation""": TFBlenderbotForConditionalGeneration,
				    }
				    if is_tf_available()
				    else {}
				)
				__a							: List[Any]										=True
				__a							: Optional[int]										=False
				__a							: Union[str, Any]										=False



				def       __snake_case		( self						):
											lowerCAmelCase   =		TFBlenderbotModelTester(self						)
											lowerCAmelCase   =		ConfigTester(self       , config_class=UpperCAmelCase_						)



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






				def       __snake_case		( 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'''



				__a							: Dict										=["""My friends are cool but they eat too many carbs."""]
				__a							: Union[str, Any]										="""facebook/blenderbot-400M-distill"""



				@cached_property
				def       __snake_case		( self						):
											return BlenderbotTokenizer.from_pretrained(self.model_name						)



				@cached_property
				def       __snake_case		( self						):
											lowerCAmelCase   =		TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name						)
											return model






				@slow
				def       __snake_case		( self						):
											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=UpperCAmelCase_						)[0]
											assert (
											    generated_words
											    == " That's unfortunate. Are they trying to lose weight or are they just trying to be healthier?"
											)





import torch

from diffusers import StableDiffusionPipeline


UpperCAmelCase_						="""path-to-your-trained-model"""
UpperCAmelCase_						=StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to("""cuda""")

UpperCAmelCase_						="""A photo of sks dog in a bucket"""
UpperCAmelCase_						=pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]

image.save("""dog-bucket.png""")



