

import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
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 DeformableDetrImageProcessor

class 					a	(						unittest.TestCase					):


						def __init__(							self    ,       A_    ,       A_=7    ,       A_=3    ,       A_=30    ,       A_=400    ,       A_=True    ,       A_=None    ,       A_=True    ,       A_=[0.5, 0.5, 0.5]    ,       A_=[0.5, 0.5, 0.5]    ,       A_=True    ,       A_=1 / 255    ,       A_=True    ,       ):







										'''simple docstring'''


										_UpperCAmelCase							:		Dict					=     size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 1333}
										_UpperCAmelCase							:		Tuple					=     parent
										_UpperCAmelCase							:		Dict					=     batch_size
										_UpperCAmelCase							:		Optional[Any]					=     num_channels
										_UpperCAmelCase							:		Any					=     min_resolution
										_UpperCAmelCase							:		Union[str, Any]					=     max_resolution
										_UpperCAmelCase							:		Union[str, Any]					=     do_resize
										_UpperCAmelCase							:		Dict					=     size
										_UpperCAmelCase							:		Optional[Any]					=     do_normalize
										_UpperCAmelCase							:		str					=     image_mean
										_UpperCAmelCase							:		Union[str, Any]					=     image_std
										_UpperCAmelCase							:		str					=     do_rescale
										_UpperCAmelCase							:		Dict					=     rescale_factor
										_UpperCAmelCase							:		Union[str, Any]					=     do_pad


						def 							_UpperCAmelCase					(							self						):







										'''simple docstring'''


										return {
										    "do_resize": self.do_resize,
										    "size": self.size,
										    "do_normalize": self.do_normalize,
										    "image_mean": self.image_mean,
										    "image_std": self.image_std,
										    "do_rescale": self.do_rescale,
										    "rescale_factor": self.rescale_factor,
										    "do_pad": self.do_pad,
										}





						def 							_UpperCAmelCase					(							self    ,       A_    ,       A_=False						):







										'''simple docstring'''


										if not batched:
														_UpperCAmelCase							:		Optional[Any]					=     image_inputs[0]
														if isinstance(a__    ,       Image.Image						):
																		_UpperCAmelCase							:		Dict					=     image.size
														else:
																		_UpperCAmelCase							:		Optional[int]					=     image.shape[1], image.shape[2]
														if w < h:
																		_UpperCAmelCase							:		Union[str, Any]					=     int(self.size["shortest_edge"] * h / w						)
																		_UpperCAmelCase							:		str					=     self.size['''shortest_edge''']
														elif w > h:
																		_UpperCAmelCase							:		Tuple					=     self.size['''shortest_edge''']
																		_UpperCAmelCase							:		Dict					=     int(self.size["shortest_edge"] * w / h						)
														else:
																		_UpperCAmelCase							:		List[Any]					=     self.size['''shortest_edge''']
																		_UpperCAmelCase							:		Any					=     self.size['''shortest_edge''']

										else:
														_UpperCAmelCase							:		Dict					=     []
														for image in image_inputs:
																		_UpperCAmelCase							:		Tuple					=     self.get_expected_values([image]						)
																		expected_values.append((expected_height, expected_width)						)
														_UpperCAmelCase							:		Optional[Any]					=     max(a__    ,       key=lambda A_						: item[0]						)[0]
														_UpperCAmelCase							:		str					=     max(a__    ,       key=lambda A_						: item[1]						)[1]

										return expected_height, expected_width







@require_torch
@require_vision
class 					a	(						lowerCamelCase__				,			unittest.TestCase					):
						_lowercase         =      DeformableDetrImageProcessor if is_vision_available() else None


						def 							_UpperCAmelCase					(							self						):







										'''simple docstring'''


										_UpperCAmelCase							:		Union[str, Any]					=     DeformableDetrImageProcessingTester(self						)


						@property
						def 							_UpperCAmelCase					(							self						):







										'''simple docstring'''


										return self.image_processor_tester.prepare_image_processor_dict()


						def 							_UpperCAmelCase					(							self						):







										'''simple docstring'''


										_UpperCAmelCase							:		List[str]					=     self.image_processing_class(**self.image_processor_dict						)
										self.assertTrue(hasattr(a__    ,       "image_mean"						)						)
										self.assertTrue(hasattr(a__    ,       "image_std"						)						)
										self.assertTrue(hasattr(a__    ,       "do_normalize"						)						)
										self.assertTrue(hasattr(a__    ,       "do_resize"						)						)
										self.assertTrue(hasattr(a__    ,       "do_rescale"						)						)
										self.assertTrue(hasattr(a__    ,       "do_pad"						)						)
										self.assertTrue(hasattr(a__    ,       "size"						)						)


						def 							_UpperCAmelCase					(							self						):







										'''simple docstring'''


										_UpperCAmelCase							:		Any					=     self.image_processing_class.from_dict(self.image_processor_dict						)
										self.assertEqual(image_processor.size    ,       {"shortest_edge": 18, "longest_edge": 1333}						)
										self.assertEqual(image_processor.do_pad    ,       a__						)

										_UpperCAmelCase							:		List[Any]					=     self.image_processing_class.from_dict(
										    self.image_processor_dict    ,       size=42    ,       max_size=84    ,       pad_and_return_pixel_mask=a__						)
										self.assertEqual(image_processor.size    ,       {"shortest_edge": 42, "longest_edge": 84}						)
										self.assertEqual(image_processor.do_pad    ,       a__						)


						def 							_UpperCAmelCase					(							self						):







										'''simple docstring'''


										pass


						def 							_UpperCAmelCase					(							self						):







										'''simple docstring'''


										_UpperCAmelCase							:		List[str]					=     self.image_processing_class(**self.image_processor_dict						)
										# create random PIL images
										_UpperCAmelCase							:		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
										_UpperCAmelCase							:		List[str]					=     image_processing(image_inputs[0]    ,       return_tensors="pt"						).pixel_values

										_UpperCAmelCase							:		int					=     self.image_processor_tester.get_expected_values(a__						)

										self.assertEqual(
										    encoded_images.shape    ,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)    ,       )

										# Test batched
										_UpperCAmelCase							:		Optional[int]					=     self.image_processor_tester.get_expected_values(a__    ,       batched=a__						)

										_UpperCAmelCase							:		Union[str, 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,
										        expected_height,
										        expected_width,
										    )    ,       )


						def 							_UpperCAmelCase					(							self						):







										'''simple docstring'''


										_UpperCAmelCase							:		List[Any]					=     self.image_processing_class(**self.image_processor_dict						)
										# create random numpy tensors
										_UpperCAmelCase							:		Optional[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
										_UpperCAmelCase							:		List[Any]					=     image_processing(image_inputs[0]    ,       return_tensors="pt"						).pixel_values

										_UpperCAmelCase							:		List[str]					=     self.image_processor_tester.get_expected_values(a__						)

										self.assertEqual(
										    encoded_images.shape    ,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)    ,       )

										# Test batched
										_UpperCAmelCase							:		List[Any]					=     image_processing(a__    ,       return_tensors="pt"						).pixel_values

										_UpperCAmelCase							:		int					=     self.image_processor_tester.get_expected_values(a__    ,       batched=a__						)

										self.assertEqual(
										    encoded_images.shape    ,       (
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        expected_height,
										        expected_width,
										    )    ,       )


						def 							_UpperCAmelCase					(							self						):







										'''simple docstring'''


										_UpperCAmelCase							:		Dict					=     self.image_processing_class(**self.image_processor_dict						)
										# create random PyTorch tensors
										_UpperCAmelCase							:		List[str]					=     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
										_UpperCAmelCase							:		Tuple					=     image_processing(image_inputs[0]    ,       return_tensors="pt"						).pixel_values

										_UpperCAmelCase							:		Any					=     self.image_processor_tester.get_expected_values(a__						)

										self.assertEqual(
										    encoded_images.shape    ,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)    ,       )

										# Test batched
										_UpperCAmelCase							:		Union[str, Any]					=     image_processing(a__    ,       return_tensors="pt"						).pixel_values

										_UpperCAmelCase							:		Optional[Any]					=     self.image_processor_tester.get_expected_values(a__    ,       batched=a__						)

										self.assertEqual(
										    encoded_images.shape    ,       (
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        expected_height,
										        expected_width,
										    )    ,       )


						@slow
						def 							_UpperCAmelCase					(							self						):







										'''simple docstring'''


										_UpperCAmelCase							:		Optional[Any]					=     Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"						)
										with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"    ,       "r"						) as f:
														_UpperCAmelCase							:		int					=     json.loads(f.read()						)

										_UpperCAmelCase							:		Tuple					=     {'''image_id''': 39769, '''annotations''': target}

										# encode them
										_UpperCAmelCase							:		List[Any]					=     DeformableDetrImageProcessor()
										_UpperCAmelCase							:		Any					=     image_processing(images=a__    ,       annotations=a__    ,       return_tensors="pt"						)

										# verify pixel values
										_UpperCAmelCase							:		Union[str, Any]					=     torch.Size([1, 3, 800, 1066]						)
										self.assertEqual(encoding["pixel_values"].shape    ,       a__						)

										_UpperCAmelCase							:		List[str]					=     torch.tensor([0.27_96, 0.31_38, 0.34_81]						)
										self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]    ,       a__    ,       atol=1e-4						)						)

										# verify area
										_UpperCAmelCase							:		str					=     torch.tensor([58_87.96_00, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]						)
										self.assertTrue(torch.allclose(encoding["labels"][0]["area"]    ,       a__						)						)
										# verify boxes
										_UpperCAmelCase							:		Any					=     torch.Size([6, 4]						)
										self.assertEqual(encoding["labels"][0]["boxes"].shape    ,       a__						)
										_UpperCAmelCase							:		Dict					=     torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15]						)
										self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]    ,       a__    ,       atol=1e-3						)						)
										# verify image_id
										_UpperCAmelCase							:		Union[str, Any]					=     torch.tensor([39769]						)
										self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]    ,       a__						)						)
										# verify is_crowd
										_UpperCAmelCase							:		str					=     torch.tensor([0, 0, 0, 0, 0, 0]						)
										self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]    ,       a__						)						)
										# verify class_labels
										_UpperCAmelCase							:		List[str]					=     torch.tensor([75, 75, 63, 65, 17, 17]						)
										self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]    ,       a__						)						)
										# verify orig_size
										_UpperCAmelCase							:		Dict					=     torch.tensor([480, 640]						)
										self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]    ,       a__						)						)
										# verify size
										_UpperCAmelCase							:		List[str]					=     torch.tensor([800, 1066]						)
										self.assertTrue(torch.allclose(encoding["labels"][0]["size"]    ,       a__						)						)





						@slow
						def 							_UpperCAmelCase					(							self						):







										'''simple docstring'''


										_UpperCAmelCase							:		int					=     Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"						)
										with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"    ,       "r"						) as f:
														_UpperCAmelCase							:		Optional[int]					=     json.loads(f.read()						)

										_UpperCAmelCase							:		Tuple					=     {'''file_name''': '''000000039769.png''', '''image_id''': 39769, '''segments_info''': target}

										_UpperCAmelCase							:		Dict					=     pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic"						)

										# encode them
										_UpperCAmelCase							:		Dict					=     DeformableDetrImageProcessor(format="coco_panoptic"						)
										_UpperCAmelCase							:		Optional[int]					=     image_processing(images=a__    ,       annotations=a__    ,       masks_path=a__    ,       return_tensors="pt"						)

										# verify pixel values
										_UpperCAmelCase							:		Optional[Any]					=     torch.Size([1, 3, 800, 1066]						)
										self.assertEqual(encoding["pixel_values"].shape    ,       a__						)

										_UpperCAmelCase							:		Any					=     torch.tensor([0.27_96, 0.31_38, 0.34_81]						)
										self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]    ,       a__    ,       atol=1e-4						)						)

										# verify area
										_UpperCAmelCase							:		Optional[int]					=     torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 58_79.65_62, 76_34.11_47]						)
										self.assertTrue(torch.allclose(encoding["labels"][0]["area"]    ,       a__						)						)
										# verify boxes
										_UpperCAmelCase							:		Optional[int]					=     torch.Size([6, 4]						)
										self.assertEqual(encoding["labels"][0]["boxes"].shape    ,       a__						)
										_UpperCAmelCase							:		Tuple					=     torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25]						)
										self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]    ,       a__    ,       atol=1e-3						)						)
										# verify image_id
										_UpperCAmelCase							:		Union[str, Any]					=     torch.tensor([39769]						)
										self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]    ,       a__						)						)
										# verify is_crowd
										_UpperCAmelCase							:		Optional[Any]					=     torch.tensor([0, 0, 0, 0, 0, 0]						)
										self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]    ,       a__						)						)
										# verify class_labels
										_UpperCAmelCase							:		Dict					=     torch.tensor([17, 17, 63, 75, 75, 93]						)
										self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]    ,       a__						)						)
										# verify masks
										_UpperCAmelCase							:		Union[str, Any]					=     822873
										self.assertEqual(encoding["labels"][0]["masks"].sum().item()    ,       a__						)
										# verify orig_size
										_UpperCAmelCase							:		List[Any]					=     torch.tensor([480, 640]						)
										self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]    ,       a__						)						)
										# verify size
										_UpperCAmelCase							:		Dict					=     torch.tensor([800, 1066]						)
										self.assertTrue(torch.allclose(encoding["labels"][0]["size"]    ,       a__						)						)









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.",
             )

