






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 		_snake_case  :
			_lowercase		:    Tuple			     =							BlenderbotSmallConfig
			_lowercase		:    Dict			     =							{}
			_lowercase		:    Optional[int]			     =							'''gelu'''






			def __init__(       self		,					a		,					a=13		,					a=7		,					a=True		,					a=False		,					a=99		,					a=32		,					a=2		,					a=4		,					a=37		,					a=0.1		,					a=0.1		,					a=20		,					a=2		,					a=1		,					a=0		,					)      ->       List[str]:
						SCREAMING_SNAKE_CASE						= parent
						SCREAMING_SNAKE_CASE						= batch_size
						SCREAMING_SNAKE_CASE						= seq_length
						SCREAMING_SNAKE_CASE						= is_training
						SCREAMING_SNAKE_CASE						= use_labels
						SCREAMING_SNAKE_CASE						= vocab_size
						SCREAMING_SNAKE_CASE						= hidden_size
						SCREAMING_SNAKE_CASE						= num_hidden_layers
						SCREAMING_SNAKE_CASE						= num_attention_heads
						SCREAMING_SNAKE_CASE						= intermediate_size

						SCREAMING_SNAKE_CASE						= hidden_dropout_prob
						SCREAMING_SNAKE_CASE						= attention_probs_dropout_prob
						SCREAMING_SNAKE_CASE						= max_position_embeddings
						SCREAMING_SNAKE_CASE						= eos_token_id
						SCREAMING_SNAKE_CASE						= pad_token_id
						SCREAMING_SNAKE_CASE						= bos_token_id






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Dict:
						SCREAMING_SNAKE_CASE						= ids_tensor([self.batch_size, self.seq_length - 1]		,					self.vocab_size)
						SCREAMING_SNAKE_CASE						= tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size)		,					1)
						SCREAMING_SNAKE_CASE						= tf.concat([input_ids, eos_tensor]		,					axis=1)

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

						SCREAMING_SNAKE_CASE						= 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		,					)
						SCREAMING_SNAKE_CASE						= prepare_blenderbot_small_inputs_dict(a		,					a		,					a)
						return config, inputs_dict







			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a)      ->       Union[str, Any]:
						SCREAMING_SNAKE_CASE						= TFBlenderbotSmallModel(config=a).get_decoder()
						SCREAMING_SNAKE_CASE						= inputs_dict['input_ids']

						SCREAMING_SNAKE_CASE						= input_ids[:1, :]
						SCREAMING_SNAKE_CASE						= inputs_dict['attention_mask'][:1, :]
						SCREAMING_SNAKE_CASE						= inputs_dict['head_mask']
						SCREAMING_SNAKE_CASE						= 1

						# first forward pass
						SCREAMING_SNAKE_CASE						= model(a		,					attention_mask=a		,					head_mask=a		,					use_cache=a)

						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= outputs.to_tuple()

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

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

						SCREAMING_SNAKE_CASE						= model(a		,					attention_mask=a)[0]
						SCREAMING_SNAKE_CASE						= model(a		,					attention_mask=a		,					past_key_values=a)[0]

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

						# select random slice
						SCREAMING_SNAKE_CASE						= int(ids_tensor((1,)		,					output_from_past.shape[-1]))
						SCREAMING_SNAKE_CASE						= output_from_no_past[:, -3:, random_slice_idx]
						SCREAMING_SNAKE_CASE						= output_from_past[:, :, random_slice_idx]

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






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






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Tuple:
						SCREAMING_SNAKE_CASE						= TFBlenderbotSmallModelTester(self)
						SCREAMING_SNAKE_CASE						= ConfigTester(self		,					config_class=a)






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Dict:
						self.config_tester.run_common_tests()







			def    SCREAMING_SNAKE_CASE__    (       self)      ->       List[str]:
						SCREAMING_SNAKE_CASE						= self.model_tester.prepare_config_and_inputs_for_common()
						self.model_tester.check_decoder_model_past_large_inputs(*a)



@require_tokenizers
@require_tf
class 		_snake_case  (  unittest.TestCase					):
			_lowercase		:    List[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?'''
			]
			_lowercase		:    Optional[Any]			     =							'''facebook/blenderbot_small-90M'''






			@cached_property
			def    SCREAMING_SNAKE_CASE__    (       self)      ->       List[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)      ->       Union[str, Any]:
						SCREAMING_SNAKE_CASE						= TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name)
						return model







			@slow
			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Union[str, Any]:
						SCREAMING_SNAKE_CASE						= self.tokenizer(self.src_text		,					return_tensors='tf')
						SCREAMING_SNAKE_CASE						= self.model.generate(
						    model_inputs.input_ids		,					attention_mask=model_inputs.attention_mask		,					num_beams=2		,					use_cache=a		,					)
						SCREAMING_SNAKE_CASE						= self.tokenizer.batch_decode(generated_ids.numpy()		,					skip_special_tokens=a)[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 unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
				import torch

if is_vision_available():
				from PIL import Image

				from transformers import MobileViTImageProcessor

class 		_snake_case  (  unittest.TestCase					):






			def __init__(       self		,					a		,					a=7		,					a=3		,					a=18		,					a=30		,					a=400		,					a=True		,					a=None		,					a=True		,					a=None		,					a=True		,					)      ->       Any:
						SCREAMING_SNAKE_CASE						= size if size is not None else {'shortest_edge': 20}
						SCREAMING_SNAKE_CASE						= crop_size if crop_size is not None else {'height': 18, 'width': 18}
						SCREAMING_SNAKE_CASE						= parent
						SCREAMING_SNAKE_CASE						= batch_size
						SCREAMING_SNAKE_CASE						= num_channels
						SCREAMING_SNAKE_CASE						= image_size
						SCREAMING_SNAKE_CASE						= min_resolution
						SCREAMING_SNAKE_CASE						= max_resolution
						SCREAMING_SNAKE_CASE						= do_resize
						SCREAMING_SNAKE_CASE						= size
						SCREAMING_SNAKE_CASE						= do_center_crop
						SCREAMING_SNAKE_CASE						= crop_size
						SCREAMING_SNAKE_CASE						= do_flip_channel_order







			def    SCREAMING_SNAKE_CASE__    (       self)      ->       List[Any]:
						return {
						    "do_resize": self.do_resize,
						    "size": self.size,
						    "do_center_crop": self.do_center_crop,
						    "crop_size": self.crop_size,
						    "do_flip_channel_order": self.do_flip_channel_order,
						}



@require_torch
@require_vision
class 		_snake_case  (  A__   ,    unittest.TestCase					):
			_lowercase		:    Optional[int]			     =							MobileViTImageProcessor if is_vision_available() else None






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       List[str]:
						SCREAMING_SNAKE_CASE						= MobileViTImageProcessingTester(self)






			@property
			def    SCREAMING_SNAKE_CASE__    (       self)      ->       str:
						return self.image_processor_tester.prepare_image_processor_dict()






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       str:
						SCREAMING_SNAKE_CASE						= self.image_processing_class(**self.image_processor_dict)
						self.assertTrue(hasattr(a		,					'do_resize'))
						self.assertTrue(hasattr(a		,					'size'))
						self.assertTrue(hasattr(a		,					'do_center_crop'))
						self.assertTrue(hasattr(a		,					'center_crop'))
						self.assertTrue(hasattr(a		,					'do_flip_channel_order'))






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       str:
						SCREAMING_SNAKE_CASE						= self.image_processing_class.from_dict(self.image_processor_dict)
						self.assertEqual(image_processor.size		,					{'shortest_edge': 20})
						self.assertEqual(image_processor.crop_size		,					{'height': 18, 'width': 18})

						SCREAMING_SNAKE_CASE						= self.image_processing_class.from_dict(self.image_processor_dict		,					size=42		,					crop_size=84)
						self.assertEqual(image_processor.size		,					{'shortest_edge': 42})
						self.assertEqual(image_processor.crop_size		,					{'height': 84, 'width': 84})






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Tuple:
						pass






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Tuple:
						# Initialize image_processing
						SCREAMING_SNAKE_CASE						= self.image_processing_class(**self.image_processor_dict)
						# create random PIL images
						SCREAMING_SNAKE_CASE						= prepare_image_inputs(self.image_processor_tester		,					equal_resolution=a)
						for image in image_inputs:
									self.assertIsInstance(a		,					Image.Image)

						# Test not batched input
						SCREAMING_SNAKE_CASE						= image_processing(image_inputs[0]		,					return_tensors='pt').pixel_values
						self.assertEqual(
						    encoded_images.shape		,					(
						        1,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size['height'],
						        self.image_processor_tester.crop_size['width'],
						    )		,					)

						# Test batched
						SCREAMING_SNAKE_CASE						= image_processing(a		,					return_tensors='pt').pixel_values
						self.assertEqual(
						    encoded_images.shape		,					(
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size['height'],
						        self.image_processor_tester.crop_size['width'],
						    )		,					)






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Dict:
						# Initialize image_processing
						SCREAMING_SNAKE_CASE						= self.image_processing_class(**self.image_processor_dict)
						# create random numpy tensors
						SCREAMING_SNAKE_CASE						= prepare_image_inputs(self.image_processor_tester		,					equal_resolution=a		,					numpify=a)
						for image in image_inputs:
									self.assertIsInstance(a		,					np.ndarray)

						# Test not batched input
						SCREAMING_SNAKE_CASE						= image_processing(image_inputs[0]		,					return_tensors='pt').pixel_values
						self.assertEqual(
						    encoded_images.shape		,					(
						        1,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size['height'],
						        self.image_processor_tester.crop_size['width'],
						    )		,					)

						# Test batched
						SCREAMING_SNAKE_CASE						= image_processing(a		,					return_tensors='pt').pixel_values
						self.assertEqual(
						    encoded_images.shape		,					(
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size['height'],
						        self.image_processor_tester.crop_size['width'],
						    )		,					)







			def    SCREAMING_SNAKE_CASE__    (       self)      ->       int:
						# Initialize image_processing
						SCREAMING_SNAKE_CASE						= self.image_processing_class(**self.image_processor_dict)
						# create random PyTorch tensors
						SCREAMING_SNAKE_CASE						= prepare_image_inputs(self.image_processor_tester		,					equal_resolution=a		,					torchify=a)
						for image in image_inputs:
									self.assertIsInstance(a		,					torch.Tensor)

						# Test not batched input
						SCREAMING_SNAKE_CASE						= image_processing(image_inputs[0]		,					return_tensors='pt').pixel_values
						self.assertEqual(
						    encoded_images.shape		,					(
						        1,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size['height'],
						        self.image_processor_tester.crop_size['width'],
						    )		,					)

						# Test batched
						SCREAMING_SNAKE_CASE						= image_processing(a		,					return_tensors='pt').pixel_values
						self.assertEqual(
						    encoded_images.shape		,					(
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size['height'],
						        self.image_processor_tester.crop_size['width'],
						    )		,					)




