

"""simple docstring"""






import unittest

import numpy as np

from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
	import torch

if is_pytesseract_available():
	from PIL import Image

	from transformers import LayoutLMvaImageProcessor







class       snake_case__  ( unittest.TestCase     ):







			def __init__(			self			: Any			,  lowercase			: int			,  lowercase			: List[Any]=7			,  lowercase			: List[Any]=3			,  lowercase			: List[str]=18			,  lowercase			: int=30			,  lowercase			: Dict=4_00			,  lowercase			: int=True			,  lowercase			: int=None			,  lowercase			: List[Any]=True			,  ):





								'''simple docstring'''



								UpperCAmelCase			:   List[str]      =				size if size is not None else {"height": 18, "width": 18}
								UpperCAmelCase			:   List[Any]      =				parent
								UpperCAmelCase			:   int      =				batch_size
								UpperCAmelCase			:   int      =				num_channels
								UpperCAmelCase			:   Any      =				image_size
								UpperCAmelCase			:   Optional[Any]      =				min_resolution
								UpperCAmelCase			:   Union[str, Any]      =				max_resolution
								UpperCAmelCase			:   str      =				do_resize
								UpperCAmelCase			:   str      =				size
								UpperCAmelCase			:   Optional[Any]      =				apply_ocr







			def 			__lowerCAmelCase				(			self			: int  ):





								'''simple docstring'''



								return {"do_resize": self.do_resize, "size": self.size, "apply_ocr": self.apply_ocr}







@require_torch
@require_pytesseract
class       snake_case__  ( lowerCAmelCase_					,  unittest.TestCase     ):
			SCREAMING_SNAKE_CASE__     				=						LayoutLMvaImageProcessor if is_pytesseract_available() else None







			def 			__lowerCAmelCase				(			self			: Optional[Any]  ):





								'''simple docstring'''



								UpperCAmelCase			:   List[Any]      =				LayoutLMvaImageProcessingTester(self  )







			@property
			def 			__lowerCAmelCase				(			self			: int  ):





								'''simple docstring'''



								return self.image_processor_tester.prepare_image_processor_dict()







			def 			__lowerCAmelCase				(			self			: Any  ):





								'''simple docstring'''



								UpperCAmelCase			:   Optional[Any]      =				self.image_processing_class(**self.image_processor_dict  )
								self.assertTrue(hasattr(lowercase			,  "do_resize"  )  )
								self.assertTrue(hasattr(lowercase			,  "size"  )  )
								self.assertTrue(hasattr(lowercase			,  "apply_ocr"  )  )







			def 			__lowerCAmelCase				(			self			: Optional[Any]  ):





								'''simple docstring'''



								UpperCAmelCase			:   Optional[Any]      =				self.image_processing_class.from_dict(self.image_processor_dict  )
								self.assertEqual(image_processor.size			,  {"height": 18, "width": 18}  )

								UpperCAmelCase			:   Any      =				self.image_processing_class.from_dict(self.image_processor_dict			,  size=42  )
								self.assertEqual(image_processor.size			,  {"height": 42, "width": 42}  )







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





								'''simple docstring'''



								pass







			def 			__lowerCAmelCase				(			self			: List[str]  ):





								'''simple docstring'''



								UpperCAmelCase			:   Dict      =				self.image_processing_class(**self.image_processor_dict  )
								# create random PIL images
								UpperCAmelCase			:   Union[str, Any]      =				prepare_image_inputs(self.image_processor_tester			,  equal_resolution=lowercase  )
								for image in image_inputs:
													self.assertIsInstance(lowercase			,  Image.Image  )

								# Test not batched input
								UpperCAmelCase			:   Optional[int]      =				image_processing(image_inputs[0]			,  return_tensors="pt"  )
								self.assertEqual(
								    encoding.pixel_values.shape			,  (
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.size["height"],
								        self.image_processor_tester.size["width"],
								    )			,  )

								self.assertIsInstance(encoding.words			,  lowercase  )
								self.assertIsInstance(encoding.boxes			,  lowercase  )

								# Test batched
								UpperCAmelCase			:   List[Any]      =				image_processing(lowercase			,  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.size["height"],
								        self.image_processor_tester.size["width"],
								    )			,  )







			def 			__lowerCAmelCase				(			self			: Any  ):





								'''simple docstring'''



								UpperCAmelCase			:   Any      =				self.image_processing_class(**self.image_processor_dict  )
								# create random numpy tensors
								UpperCAmelCase			:   int      =				prepare_image_inputs(self.image_processor_tester			,  equal_resolution=lowercase			,  numpify=lowercase  )
								for image in image_inputs:
													self.assertIsInstance(lowercase			,  np.ndarray  )

								# Test not batched input
								UpperCAmelCase			:   Optional[int]      =				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.size["height"],
								        self.image_processor_tester.size["width"],
								    )			,  )

								# Test batched
								UpperCAmelCase			:   Dict      =				image_processing(lowercase			,  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.size["height"],
								        self.image_processor_tester.size["width"],
								    )			,  )







			def 			__lowerCAmelCase				(			self			: int  ):





								'''simple docstring'''



								UpperCAmelCase			:   Tuple      =				self.image_processing_class(**self.image_processor_dict  )
								# create random PyTorch tensors
								UpperCAmelCase			:   int      =				prepare_image_inputs(self.image_processor_tester			,  equal_resolution=lowercase			,  torchify=lowercase  )
								for image in image_inputs:
													self.assertIsInstance(lowercase			,  torch.Tensor  )

								# Test not batched input
								UpperCAmelCase			:   Tuple      =				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.size["height"],
								        self.image_processor_tester.size["width"],
								    )			,  )

								# Test batched
								UpperCAmelCase			:   Optional[int]      =				image_processing(lowercase			,  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.size["height"],
								        self.image_processor_tester.size["width"],
								    )			,  )







			def 			__lowerCAmelCase				(			self			: List[Any]  ):





								'''simple docstring'''



								UpperCAmelCase			:   Tuple      =				LayoutLMvaImageProcessor()

								from datasets import load_dataset

								UpperCAmelCase			:   Union[str, Any]      =				load_dataset("hf-internal-testing/fixtures_docvqa"			,  split="test"  )

								UpperCAmelCase			:   List[str]      =				Image.open(ds[0]["file"]  ).convert("RGB"  )

								UpperCAmelCase			:   Any      =				image_processing(lowercase			,  return_tensors="pt"  )

								self.assertEqual(encoding.pixel_values.shape			,  (1, 3, 2_24, 2_24)  )
								self.assertEqual(len(encoding.words  )			,  len(encoding.boxes  )  )

								# fmt: off
								# the words and boxes were obtained with Tesseract 4.1.1
								UpperCAmelCase			:   Dict      =				[["11:14", "to", "11:39", "a.m", "11:39", "to", "11:44", "a.m.", "11:44", "a.m.", "to", "12:25", "p.m.", "12:25", "to", "12:58", "p.m.", "12:58", "to", "4:00", "p.m.", "2:00", "to", "5:00", "p.m.", "Coffee", "Break", "Coffee", "will", "be", "served", "for", "men", "and", "women", "in", "the", "lobby", "adjacent", "to", "exhibit", "area.", "Please", "move", "into", "exhibit", "area.", "(Exhibits", "Open)", "TRRF", "GENERAL", "SESSION", "(PART", "|)", "Presiding:", "Lee", "A.", "Waller", "TRRF", "Vice", "President", "“Introductory", "Remarks”", "Lee", "A.", "Waller,", "TRRF", "Vice", "Presi-", "dent", "Individual", "Interviews", "with", "TRRF", "Public", "Board", "Members", "and", "Sci-", "entific", "Advisory", "Council", "Mem-", "bers", "Conducted", "by", "TRRF", "Treasurer", "Philip", "G.", "Kuehn", "to", "get", "answers", "which", "the", "public", "refrigerated", "warehousing", "industry", "is", "looking", "for.", "Plus", "questions", "from", "the", "floor.", "Dr.", "Emil", "M.", "Mrak,", "University", "of", "Cal-", "ifornia,", "Chairman,", "TRRF", "Board;", "Sam", "R.", "Cecil,", "University", "of", "Georgia", "College", "of", "Agriculture;", "Dr.", "Stanley", "Charm,", "Tufts", "University", "School", "of", "Medicine;", "Dr.", "Robert", "H.", "Cotton,", "ITT", "Continental", "Baking", "Company;", "Dr.", "Owen", "Fennema,", "University", "of", "Wis-", "consin;", "Dr.", "Robert", "E.", "Hardenburg,", "USDA.", "Questions", "and", "Answers", "Exhibits", "Open", "Capt.", "Jack", "Stoney", "Room", "TRRF", "Scientific", "Advisory", "Council", "Meeting", "Ballroom", "Foyer"]]  # noqa: E231
								UpperCAmelCase			:   List[Any]      =				[[[1_41, 57, 2_14, 69], [2_28, 58, 2_52, 69], [1_41, 75, 2_16, 88], [2_30, 79, 2_80, 88], [1_42, 2_60, 2_18, 2_73], [2_30, 2_61, 2_55, 2_73], [1_43, 2_79, 2_18, 2_90], [2_31, 2_82, 2_90, 2_91], [1_43, 3_42, 2_18, 3_54], [2_31, 3_45, 2_89, 3_55], [2_02, 3_62, 2_27, 3_73], [1_43, 3_79, 2_20, 3_92], [2_31, 3_82, 2_91, 3_94], [1_44, 7_14, 2_20, 7_26], [2_31, 7_15, 2_56, 7_26], [1_44, 7_32, 2_20, 7_45], [2_32, 7_36, 2_91, 7_47], [1_44, 7_69, 2_18, 7_82], [2_31, 7_70, 2_56, 7_82], [1_41, 7_88, 2_02, 8_01], [2_15, 7_91, 2_74, 8_04], [1_43, 8_26, 2_04, 8_38], [2_15, 8_26, 2_40, 8_38], [1_42, 8_44, 2_02, 8_57], [2_15, 8_47, 2_74, 8_59], [3_34, 57, 4_27, 69], [4_40, 57, 5_22, 69], [3_69, 75, 4_61, 88], [4_69, 75, 5_16, 88], [5_28, 76, 5_62, 88], [5_70, 76, 6_67, 88], [6_75, 75, 7_11, 87], [7_21, 79, 7_78, 88], [7_89, 75, 8_40, 88], [3_69, 97, 4_70, 1_07], [4_84, 94, 5_07, 1_06], [5_18, 94, 5_62, 1_07], [5_76, 94, 6_55, 1_10], [6_68, 94, 7_92, 1_09], [8_04, 95, 8_29, 1_07], [3_69, 1_13, 4_65, 1_25], [4_77, 1_16, 5_47, 1_25], [5_62, 1_13, 6_58, 1_25], [6_71, 1_16, 7_48, 1_25], [7_61, 1_13, 8_11, 1_25], [3_69, 1_31, 4_65, 1_43], [4_77, 1_33, 5_48, 1_43], [5_63, 1_30, 6_98, 1_45], [7_10, 1_30, 8_02, 1_46], [3_36, 1_71, 4_12, 1_83], [4_23, 1_71, 5_72, 1_83], [5_82, 1_70, 7_16, 1_84], [7_28, 1_71, 8_17, 1_87], [8_29, 1_71, 8_44, 1_86], [3_38, 1_97, 4_82, 2_12], [5_07, 1_96, 5_57, 2_09], [5_69, 1_96, 5_95, 2_08], [6_10, 1_96, 7_02, 2_09], [5_05, 2_14, 5_83, 2_26], [5_95, 2_14, 6_56, 2_27], [6_70, 2_15, 8_07, 2_27], [3_35, 2_59, 5_43, 2_74], [5_56, 2_59, 7_08, 2_72], [3_72, 2_79, 4_22, 2_91], [4_35, 2_79, 4_60, 2_91], [4_74, 2_79, 5_74, 2_92], [5_87, 2_78, 6_64, 2_91], [6_76, 2_78, 7_38, 2_91], [7_51, 2_79, 8_34, 2_91], [3_72, 2_98, 4_34, 3_10], [3_35, 3_41, 4_83, 3_54], [4_97, 3_41, 6_55, 3_54], [6_67, 3_41, 7_28, 3_54], [7_40, 3_41, 8_25, 3_54], [3_35, 3_60, 4_30, 3_72], [4_42, 3_60, 5_34, 3_72], [5_45, 3_59, 6_87, 3_72], [6_97, 3_60, 7_54, 3_72], [7_65, 3_60, 8_23, 3_73], [3_34, 3_78, 4_28, 3_91], [4_40, 3_78, 5_77, 3_94], [5_90, 3_78, 7_05, 3_91], [7_20, 3_78, 8_01, 3_91], [3_34, 3_97, 4_00, 4_09], [3_70, 4_16, 5_29, 4_29], [5_44, 4_16, 5_76, 4_32], [5_87, 4_16, 6_65, 4_28], [6_77, 4_16, 8_14, 4_29], [3_72, 4_35, 4_52, 4_50], [4_65, 4_34, 4_95, 4_47], [5_11, 4_34, 6_00, 4_47], [6_11, 4_36, 6_37, 4_47], [6_49, 4_36, 6_94, 4_51], [7_05, 4_38, 8_24, 4_47], [3_69, 4_53, 4_52, 4_66], [4_64, 4_54, 5_09, 4_66], [5_22, 4_53, 6_11, 4_69], [6_25, 4_53, 7_92, 4_69], [3_70, 4_72, 5_56, 4_88], [5_70, 4_72, 6_84, 4_87], [6_97, 4_72, 7_18, 4_85], [7_32, 4_72, 8_35, 4_88], [3_69, 4_90, 4_11, 5_03], [4_25, 4_90, 4_84, 5_03], [4_96, 4_90, 6_35, 5_06], [6_45, 4_90, 7_07, 5_03], [7_18, 4_91, 7_61, 5_03], [7_71, 4_90, 8_40, 5_03], [3_36, 5_10, 3_74, 5_21], [3_88, 5_10, 4_47, 5_22], [4_60, 5_10, 4_89, 5_21], [5_03, 5_10, 5_80, 5_22], [5_92, 5_09, 7_36, 5_25], [7_45, 5_09, 7_70, 5_22], [7_81, 5_09, 8_40, 5_22], [3_38, 5_28, 4_34, 5_41], [4_48, 5_28, 5_96, 5_41], [6_09, 5_27, 6_87, 5_40], [7_00, 5_28, 7_92, 5_41], [3_36, 5_46, 3_97, 5_59], [4_07, 5_46, 4_31, 5_59], [4_43, 5_46, 5_25, 5_60], [5_37, 5_46, 6_80, 5_62], [6_88, 5_46, 7_14, 5_59], [7_22, 5_46, 8_37, 5_62], [3_36, 5_65, 4_49, 5_81], [4_61, 5_65, 4_85, 5_77], [4_97, 5_65, 6_65, 5_81], [6_81, 5_65, 7_18, 5_77], [7_32, 5_65, 8_37, 5_80], [3_37, 5_84, 4_38, 5_97], [4_52, 5_83, 5_21, 5_96], [5_35, 5_84, 6_77, 5_99], [6_90, 5_83, 7_87, 5_96], [8_01, 5_83, 8_25, 5_96], [3_38, 6_02, 4_78, 6_15], [4_92, 6_02, 5_30, 6_14], [5_43, 6_02, 6_38, 6_15], [6_50, 6_02, 6_76, 6_14], [6_88, 6_02, 7_88, 6_15], [8_02, 6_02, 8_43, 6_14], [3_37, 6_21, 5_02, 6_33], [5_16, 6_21, 6_15, 6_37], [6_29, 6_21, 7_74, 6_36], [7_89, 6_21, 8_27, 6_33], [3_37, 6_39, 4_18, 6_52], [4_32, 6_40, 5_71, 6_53], [5_87, 6_39, 7_31, 6_55], [7_43, 6_39, 7_69, 6_52], [7_80, 6_39, 8_41, 6_52], [3_38, 6_58, 4_40, 6_73], [4_55, 6_58, 4_91, 6_70], [5_08, 6_58, 6_02, 6_71], [6_16, 6_58, 6_38, 6_70], [6_54, 6_58, 8_35, 6_74], [3_37, 6_77, 4_29, 6_89], [3_37, 7_14, 4_82, 7_26], [4_95, 7_14, 5_48, 7_26], [5_61, 7_14, 6_83, 7_26], [3_38, 7_70, 4_61, 7_82], [4_74, 7_69, 5_54, 7_85], [4_89, 7_88, 5_62, 8_03], [5_76, 7_88, 6_43, 8_01], [6_56, 7_87, 7_51, 8_04], [7_64, 7_88, 8_44, 8_01], [3_34, 8_25, 4_21, 8_38], [4_30, 8_24, 5_74, 8_38], [5_84, 8_24, 7_23, 8_41], [3_35, 8_44, 4_50, 8_57], [4_64, 8_43, 5_83, 8_60], [6_28, 8_62, 7_55, 8_75], [7_69, 8_61, 8_48, 8_78]]]  # noqa: E231
								# fmt: on

								self.assertListEqual(encoding.words			,  lowercase  )
								self.assertListEqual(encoding.boxes			,  lowercase  )

								# with apply_OCR = False
								UpperCAmelCase			:   List[str]      =				LayoutLMvaImageProcessor(apply_ocr=lowercase  )

								UpperCAmelCase			:   Optional[int]      =				image_processing(lowercase			,  return_tensors="pt"  )

								self.assertEqual(encoding.pixel_values.shape			,  (1, 3, 2_24, 2_24)  )





"""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       snake_case__  :
			SCREAMING_SNAKE_CASE__     				=						BlenderbotSmallConfig
			SCREAMING_SNAKE_CASE__     				=						{}
			SCREAMING_SNAKE_CASE__     				=						'''gelu'''







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





								'''simple docstring'''



								UpperCAmelCase			:   Dict      =				parent
								UpperCAmelCase			:   Union[str, Any]      =				batch_size
								UpperCAmelCase			:   Tuple      =				seq_length
								UpperCAmelCase			:   Dict      =				is_training
								UpperCAmelCase			:   str      =				use_labels
								UpperCAmelCase			:   Union[str, Any]      =				vocab_size
								UpperCAmelCase			:   Tuple      =				hidden_size
								UpperCAmelCase			:   List[Any]      =				num_hidden_layers
								UpperCAmelCase			:   Union[str, Any]      =				num_attention_heads
								UpperCAmelCase			:   Dict      =				intermediate_size

								UpperCAmelCase			:   Optional[int]      =				hidden_dropout_prob
								UpperCAmelCase			:   Optional[Any]      =				attention_probs_dropout_prob
								UpperCAmelCase			:   Dict      =				max_position_embeddings
								UpperCAmelCase			:   List[Any]      =				eos_token_id
								UpperCAmelCase			:   List[Any]      =				pad_token_id
								UpperCAmelCase			:   Union[str, Any]      =				bos_token_id







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





								'''simple docstring'''



								UpperCAmelCase			:   List[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			:   Optional[int]      =				tf.concat([input_ids, eos_tensor]			,  axis=1  )

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

								UpperCAmelCase			:   int      =				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			:   Optional[Any]      =				prepare_blenderbot_small_inputs_dict(lowercase			,  lowercase			,  lowercase  )
								return config, inputs_dict







			def 			__lowerCAmelCase				(			self			: int			,  lowercase			: List[Any]			,  lowercase			: Optional[Any]  ):





								'''simple docstring'''



								UpperCAmelCase			:   List[Any]      =				TFBlenderbotSmallModel(config=lowercase  ).get_decoder()
								UpperCAmelCase			:   List[Any]      =				inputs_dict["input_ids"]

								UpperCAmelCase			:   int      =				input_ids[:1, :]
								UpperCAmelCase			:   Union[str, Any]      =				inputs_dict["attention_mask"][:1, :]
								UpperCAmelCase			:   Union[str, Any]      =				inputs_dict["head_mask"]
								UpperCAmelCase			:   List[Any]      =				1

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

								UpperCAmelCase      ,							UpperCAmelCase			:   List[str]      =				outputs.to_tuple()

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

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

								UpperCAmelCase			:   Optional[Any]      =				model(lowercase			,  attention_mask=lowercase  )[0]
								UpperCAmelCase			:   List[str]      =				model(lowercase			,  attention_mask=lowercase			,  past_key_values=lowercase  )[0]

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

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

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



def   lowercase_	(    _lowercase       :		Optional[Any]							,      _lowercase       :		Optional[int]							,      _lowercase       :		Tuple							,      _lowercase       :		Tuple=None							,      _lowercase       :		Any=None							,      _lowercase       :		str=None							,      _lowercase       :		Optional[Any]=None							,      _lowercase       :		Dict=None							,      ):




					'''simple docstring'''






					if attention_mask is None:
										UpperCAmelCase			:   List[str]      =				tf.cast(tf.math.not_equal(_lowercase							,      config.pad_token_id	)							,      tf.inta	)
					if decoder_attention_mask is None:
										UpperCAmelCase			:   str      =				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			:   Dict      =				tf.ones((config.encoder_layers, config.encoder_attention_heads)	)
					if decoder_head_mask is None:
										UpperCAmelCase			:   Optional[Any]      =				tf.ones((config.decoder_layers, config.decoder_attention_heads)	)
					if cross_attn_head_mask is None:
										UpperCAmelCase			:   str      =				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__  ( lowerCAmelCase_					,  lowerCAmelCase_					,  unittest.TestCase     ):
			SCREAMING_SNAKE_CASE__     				=						(
			    (TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel) if is_tf_available() else ()
			)
			SCREAMING_SNAKE_CASE__     				=						(TFBlenderbotSmallForConditionalGeneration,) if is_tf_available() else ()
			SCREAMING_SNAKE_CASE__     				=						(
			    {
			        '''conversational''': TFBlenderbotSmallForConditionalGeneration,
			        '''feature-extraction''': TFBlenderbotSmallModel,
			        '''summarization''': TFBlenderbotSmallForConditionalGeneration,
			        '''text2text-generation''': TFBlenderbotSmallForConditionalGeneration,
			        '''translation''': TFBlenderbotSmallForConditionalGeneration,
			    }
			    if is_tf_available()
			    else {}
			)
			SCREAMING_SNAKE_CASE__     				=						True
			SCREAMING_SNAKE_CASE__     				=						False
			SCREAMING_SNAKE_CASE__     				=						False







			def 			__lowerCAmelCase				(			self			: Optional[Any]  ):





								'''simple docstring'''



								UpperCAmelCase			:   Dict      =				TFBlenderbotSmallModelTester(self  )
								UpperCAmelCase			:   Dict      =				ConfigTester(self			,  config_class=lowercase  )







			def 			__lowerCAmelCase				(			self			: Optional[Any]  ):





								'''simple docstring'''



								self.config_tester.run_common_tests()







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





								'''simple docstring'''



								UpperCAmelCase			:   Dict      =				self.model_tester.prepare_config_and_inputs_for_common()
								self.model_tester.check_decoder_model_past_large_inputs(*lowercase  )







@require_tokenizers
@require_tf
class       snake_case__  ( unittest.TestCase     ):
			SCREAMING_SNAKE_CASE__     				=						[
			    '''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?'''
			]
			SCREAMING_SNAKE_CASE__     				=						'''facebook/blenderbot_small-90M'''







			@cached_property
			def 			__lowerCAmelCase				(			self			: Any  ):





								'''simple docstring'''



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







			@cached_property
			def 			__lowerCAmelCase				(			self			: Any  ):





								'''simple docstring'''



								UpperCAmelCase			:   Union[str, Any]      =				TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name  )
								return model







			@slow
			def 			__lowerCAmelCase				(			self			: Optional[int]  ):





								'''simple docstring'''



								UpperCAmelCase			:   List[Any]      =				self.tokenizer(self.src_text			,  return_tensors="tf"  )
								UpperCAmelCase			:   List[Any]      =				self.model.generate(
								    model_inputs.input_ids			,  attention_mask=model_inputs.attention_mask			,  num_beams=2			,  use_cache=lowercase			,  )
								UpperCAmelCase			:   Dict      =				self.tokenizer.batch_decode(generated_ids.numpy()			,  skip_special_tokens=lowercase  )[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.",
								)


