





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       __A							(   unittest.TestCase	):




      """simple docstring"""







      def __init__(		self   ,				a__   ,				a__=7   ,				a__=3   ,				a__=18   ,				a__=30   ,				a__=400   ,				a__=True   ,				a__=None   ,				a__=True   ,				):



             """simple docstring"""





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



      def 					__snake_case      (		self):



             """simple docstring"""





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







@require_torch
@require_pytesseract
class       __A							(   lowerCamelCase__			,unittest.TestCase	):




      """simple docstring"""







      UpperCAmelCase__							=   LayoutLMvaImageProcessor if is_pytesseract_available() else None


      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  Any						=		LayoutLMvaImageProcessingTester(self)


      @property
      def 					__snake_case      (		self):



             """simple docstring"""





             return self.image_processor_tester.prepare_image_processor_dict()


      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  Any						=		self.image_processing_class(**self.image_processor_dict)
             self.assertTrue(hasattr(a__   ,				'''do_resize'''))
             self.assertTrue(hasattr(a__   ,				'''size'''))
             self.assertTrue(hasattr(a__   ,				'''apply_ocr'''))


      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  List[Any]						=		self.image_processing_class.from_dict(self.image_processor_dict)
             self.assertEqual(image_processor.size   ,				{'''height''': 18, '''width''': 18})

             _lowerCamelCase				:  int						=		self.image_processing_class.from_dict(self.image_processor_dict   ,				size=42)
             self.assertEqual(image_processor.size   ,				{'''height''': 42, '''width''': 42})


      def 					__snake_case      (		self):



             """simple docstring"""





             pass


      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  Optional[Any]						=		self.image_processing_class(**self.image_processor_dict)
             # create random PIL images
             _lowerCamelCase				:  List[Any]						=		prepare_image_inputs(self.image_processor_tester   ,				equal_resolution=a__)
             for image in image_inputs:
                    self.assertIsInstance(a__   ,				Image.Image)

             # Test not batched input
             _lowerCamelCase				:  str						=		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   ,				a__)
             self.assertIsInstance(encoding.boxes   ,				a__)

             # Test batched
             _lowerCamelCase				:  Optional[Any]						=		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.size['''height'''],
                     self.image_processor_tester.size['''width'''],
                 )   ,				)


      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  Tuple						=		self.image_processing_class(**self.image_processor_dict)
             # create random numpy tensors
             _lowerCamelCase				:  List[Any]						=		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
             _lowerCamelCase				:  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
             _lowerCamelCase				:  str						=		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.size['''height'''],
                     self.image_processor_tester.size['''width'''],
                 )   ,				)


      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  Dict						=		self.image_processing_class(**self.image_processor_dict)
             # create random PyTorch tensors
             _lowerCamelCase				:  int						=		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
             _lowerCamelCase				:  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
             _lowerCamelCase				:  int						=		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.size['''height'''],
                     self.image_processor_tester.size['''width'''],
                 )   ,				)



      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  Optional[Any]						=		LayoutLMvaImageProcessor()

             from datasets import load_dataset

             _lowerCamelCase				:  Optional[int]						=		load_dataset('''hf-internal-testing/fixtures_docvqa'''   ,				split='''test''')

             _lowerCamelCase				:  Optional[Any]						=		Image.open(ds[0]['''file''']).convert('''RGB''')

             _lowerCamelCase				:  List[str]						=		image_processing(a__   ,				return_tensors='''pt''')

             self.assertEqual(encoding.pixel_values.shape   ,				(1, 3, 224, 224))
             self.assertEqual(len(encoding.words)   ,				len(encoding.boxes))

             # fmt: off
             # the words and boxes were obtained with Tesseract 4.1.1
             _lowerCamelCase				:  List[str]						=		[['''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
             _lowerCamelCase				:  List[str]						=		[[[141, 57, 214, 69], [228, 58, 252, 69], [141, 75, 216, 88], [230, 79, 280, 88], [142, 260, 218, 273], [230, 261, 255, 273], [143, 279, 218, 290], [231, 282, 290, 291], [143, 342, 218, 354], [231, 345, 289, 355], [202, 362, 227, 373], [143, 379, 220, 392], [231, 382, 291, 394], [144, 714, 220, 726], [231, 715, 256, 726], [144, 732, 220, 745], [232, 736, 291, 747], [144, 769, 218, 782], [231, 770, 256, 782], [141, 788, 202, 801], [215, 791, 274, 804], [143, 826, 204, 838], [215, 826, 240, 838], [142, 844, 202, 857], [215, 847, 274, 859], [334, 57, 427, 69], [440, 57, 522, 69], [369, 75, 461, 88], [469, 75, 516, 88], [528, 76, 562, 88], [570, 76, 667, 88], [675, 75, 711, 87], [721, 79, 778, 88], [789, 75, 840, 88], [369, 97, 470, 107], [484, 94, 507, 106], [518, 94, 562, 107], [576, 94, 655, 110], [668, 94, 792, 109], [804, 95, 829, 107], [369, 113, 465, 125], [477, 116, 547, 125], [562, 113, 658, 125], [671, 116, 748, 125], [761, 113, 811, 125], [369, 131, 465, 143], [477, 133, 548, 143], [563, 130, 698, 145], [710, 130, 802, 146], [336, 171, 412, 183], [423, 171, 572, 183], [582, 170, 716, 184], [728, 171, 817, 187], [829, 171, 844, 186], [338, 197, 482, 212], [507, 196, 557, 209], [569, 196, 595, 208], [610, 196, 702, 209], [505, 214, 583, 226], [595, 214, 656, 227], [670, 215, 807, 227], [335, 259, 543, 274], [556, 259, 708, 272], [372, 279, 422, 291], [435, 279, 460, 291], [474, 279, 574, 292], [587, 278, 664, 291], [676, 278, 738, 291], [751, 279, 834, 291], [372, 298, 434, 310], [335, 341, 483, 354], [497, 341, 655, 354], [667, 341, 728, 354], [740, 341, 825, 354], [335, 360, 430, 372], [442, 360, 534, 372], [545, 359, 687, 372], [697, 360, 754, 372], [765, 360, 823, 373], [334, 378, 428, 391], [440, 378, 577, 394], [590, 378, 705, 391], [720, 378, 801, 391], [334, 397, 400, 409], [370, 416, 529, 429], [544, 416, 576, 432], [587, 416, 665, 428], [677, 416, 814, 429], [372, 435, 452, 450], [465, 434, 495, 447], [511, 434, 600, 447], [611, 436, 637, 447], [649, 436, 694, 451], [705, 438, 824, 447], [369, 453, 452, 466], [464, 454, 509, 466], [522, 453, 611, 469], [625, 453, 792, 469], [370, 472, 556, 488], [570, 472, 684, 487], [697, 472, 718, 485], [732, 472, 835, 488], [369, 490, 411, 503], [425, 490, 484, 503], [496, 490, 635, 506], [645, 490, 707, 503], [718, 491, 761, 503], [771, 490, 840, 503], [336, 510, 374, 521], [388, 510, 447, 522], [460, 510, 489, 521], [503, 510, 580, 522], [592, 509, 736, 525], [745, 509, 770, 522], [781, 509, 840, 522], [338, 528, 434, 541], [448, 528, 596, 541], [609, 527, 687, 540], [700, 528, 792, 541], [336, 546, 397, 559], [407, 546, 431, 559], [443, 546, 525, 560], [537, 546, 680, 562], [688, 546, 714, 559], [722, 546, 837, 562], [336, 565, 449, 581], [461, 565, 485, 577], [497, 565, 665, 581], [681, 565, 718, 577], [732, 565, 837, 580], [337, 584, 438, 597], [452, 583, 521, 596], [535, 584, 677, 599], [690, 583, 787, 596], [801, 583, 825, 596], [338, 602, 478, 615], [492, 602, 530, 614], [543, 602, 638, 615], [650, 602, 676, 614], [688, 602, 788, 615], [802, 602, 843, 614], [337, 621, 502, 633], [516, 621, 615, 637], [629, 621, 774, 636], [789, 621, 827, 633], [337, 639, 418, 652], [432, 640, 571, 653], [587, 639, 731, 655], [743, 639, 769, 652], [780, 639, 841, 652], [338, 658, 440, 673], [455, 658, 491, 670], [508, 658, 602, 671], [616, 658, 638, 670], [654, 658, 835, 674], [337, 677, 429, 689], [337, 714, 482, 726], [495, 714, 548, 726], [561, 714, 683, 726], [338, 770, 461, 782], [474, 769, 554, 785], [489, 788, 562, 803], [576, 788, 643, 801], [656, 787, 751, 804], [764, 788, 844, 801], [334, 825, 421, 838], [430, 824, 574, 838], [584, 824, 723, 841], [335, 844, 450, 857], [464, 843, 583, 860], [628, 862, 755, 875], [769, 861, 848, 878]]]  # noqa: E231
             # fmt: on

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

             # with apply_OCR = False
             _lowerCamelCase				:  List[Any]						=		LayoutLMvaImageProcessor(apply_ocr=a__)

             _lowerCamelCase				:  List[Any]						=		image_processing(a__   ,				return_tensors='''pt''')

             self.assertEqual(encoding.pixel_values.shape   ,				(1, 3, 224, 224))








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       __A							:




      """simple docstring"""







      UpperCAmelCase__							=   BlenderbotSmallConfig
      UpperCAmelCase__							=   {}
      UpperCAmelCase__							=   """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   ,				):



             """simple docstring"""





             _lowerCamelCase				:  str						=		parent
             _lowerCamelCase				:  Any						=		batch_size
             _lowerCamelCase				:  str						=		seq_length
             _lowerCamelCase				:  Optional[int]						=		is_training
             _lowerCamelCase				:  int						=		use_labels
             _lowerCamelCase				:  List[str]						=		vocab_size
             _lowerCamelCase				:  List[Any]						=		hidden_size
             _lowerCamelCase				:  Tuple						=		num_hidden_layers
             _lowerCamelCase				:  List[Any]						=		num_attention_heads
             _lowerCamelCase				:  int						=		intermediate_size

             _lowerCamelCase				:  int						=		hidden_dropout_prob
             _lowerCamelCase				:  Tuple						=		attention_probs_dropout_prob
             _lowerCamelCase				:  Dict						=		max_position_embeddings
             _lowerCamelCase				:  str						=		eos_token_id
             _lowerCamelCase				:  Any						=		pad_token_id
             _lowerCamelCase				:  str						=		bos_token_id


      def 					__snake_case      (		self):



             """simple docstring"""





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

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

             _lowerCamelCase				:  Dict						=		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				:  Optional[int]						=		prepare_blenderbot_small_inputs_dict(a__   ,				a__   ,				a__)
             return config, inputs_dict



      def 					__snake_case      (		self   ,				a__   ,				a__):



             """simple docstring"""





             _lowerCamelCase				:  Optional[int]						=		TFBlenderbotSmallModel(config=a__).get_decoder()
             _lowerCamelCase				:  Dict						=		inputs_dict['''input_ids''']

             _lowerCamelCase				:  int						=		input_ids[:1, :]
             _lowerCamelCase				:  Union[str, Any]						=		inputs_dict['''attention_mask'''][:1, :]
             _lowerCamelCase				:  List[str]						=		inputs_dict['''head_mask''']
             _lowerCamelCase				:  Optional[int]						=		1

             # first forward pass
             _lowerCamelCase				:  Union[str, Any]						=		model(a__   ,				attention_mask=a__   ,				head_mask=a__   ,				use_cache=a__)

             _lowerCamelCase,	_lowerCamelCase				:  str						=		outputs.to_tuple()

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

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

             _lowerCamelCase				:  List[str]						=		model(a__   ,				attention_mask=a__)[0]
             _lowerCamelCase				:  str						=		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
             _lowerCamelCase				:  int						=		int(ids_tensor((1,)   ,				output_from_past.shape[-1]))
             _lowerCamelCase				:  Any						=		output_from_no_past[:, -3:, random_slice_idx]
             _lowerCamelCase				:  int						=		output_from_past[:, :, random_slice_idx]

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




def  __UpperCAmelCase(   lowercase_			,     lowercase_			,     lowercase_			,     lowercase_=None			,     lowercase_=None			,     lowercase_=None			,     lowercase_=None			,     lowercase_=None			,     ):
       if attention_mask is None:
              _lowerCamelCase				:  List[Any]						=		tf.cast(tf.math.not_equal(lowercase_			,     config.pad_token_id   )			,     tf.inta   )
       if decoder_attention_mask is None:
              _lowerCamelCase				:  Union[str, 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:
              _lowerCamelCase				:  List[str]						=		tf.ones((config.encoder_layers, config.encoder_attention_heads)   )
       if decoder_head_mask is None:
              _lowerCamelCase				:  Optional[int]						=		tf.ones((config.decoder_layers, config.decoder_attention_heads)   )
       if cross_attn_head_mask is None:
              _lowerCamelCase				:  Union[str, 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       __A							(   lowerCamelCase__			,lowerCamelCase__			,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 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  List[str]						=		TFBlenderbotSmallModelTester(self)
             _lowerCamelCase				:  Optional[int]						=		ConfigTester(self   ,				config_class=a__)


      def 					__snake_case      (		self):



             """simple docstring"""





             self.config_tester.run_common_tests()



      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  Any						=		self.model_tester.prepare_config_and_inputs_for_common()
             self.model_tester.check_decoder_model_past_large_inputs(*a__)







@require_tokenizers
@require_tf
class       __A							(   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 					__snake_case      (		self):



             """simple docstring"""





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


      @cached_property
      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  List[Any]						=		TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name)
             return model



      @slow
      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  Union[str, Any]						=		self.tokenizer(self.src_text   ,				return_tensors='''tf''')
             _lowerCamelCase				:  str						=		self.model.generate(
                 model_inputs.input_ids   ,				attention_mask=model_inputs.attention_mask   ,				num_beams=2   ,				use_cache=a__   ,				)
             _lowerCamelCase				:  Tuple						=		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.",
             )

