






'''simple docstring'''





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 ConditionalDetrImageProcessor
class 				lowercase       (							unittest.TestCase      ):


				'''simple docstring'''

				def __init__(					self     :     Tuple				,     __lowerCamelCase     :     List[str]				,     __lowerCamelCase     :     Tuple=7				,     __lowerCamelCase     :     Dict=3				,     __lowerCamelCase     :     List[str]=30				,     __lowerCamelCase     :     int=400				,     __lowerCamelCase     :     List[str]=True				,     __lowerCamelCase     :     Any=None				,     __lowerCamelCase     :     Union[str, Any]=True				,     __lowerCamelCase     :     Any=[0.5, 0.5, 0.5]				,     __lowerCamelCase     :     Tuple=[0.5, 0.5, 0.5]				,     __lowerCamelCase     :     Dict=True				,     __lowerCamelCase     :     List[Any]=1 / 255				,     __lowerCamelCase     :     str=True				,     )							->							Union[str, Any]:

					'''simple docstring'''




					lowerCamelCase__  =     size if size is not None else {"shortest_edge": 18, "longest_edge": 1333}
					lowerCamelCase__  =     parent
					lowerCamelCase__  =     batch_size
					lowerCamelCase__  =     num_channels
					lowerCamelCase__  =     min_resolution
					lowerCamelCase__  =     max_resolution
					lowerCamelCase__  =     do_resize
					lowerCamelCase__  =     size
					lowerCamelCase__  =     do_normalize
					lowerCamelCase__  =     image_mean
					lowerCamelCase__  =     image_std
					lowerCamelCase__  =     do_rescale
					lowerCamelCase__  =     rescale_factor
					lowerCamelCase__  =     do_pad

				def    a__				(					self     :     Optional[int]     )							->							Dict:

					'''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    a__				(					self     :     int				,     __lowerCamelCase     :     str				,     __lowerCamelCase     :     int=False     )							->							Tuple:

					'''simple docstring'''




					if not batched:
						lowerCamelCase__  =     image_inputs[0]
						if isinstance(UpperCAmelCase_				,     Image.Image     ):
							lowerCamelCase__  ,	lowerCamelCase__  =     image.size
						else:
							lowerCamelCase__  ,	lowerCamelCase__  =     image.shape[1], image.shape[2]
						if w < h:
							lowerCamelCase__  =     int(self.size["shortest_edge"] * h / w     )
							lowerCamelCase__  =     self.size["shortest_edge"]
						elif w > h:
							lowerCamelCase__  =     self.size["shortest_edge"]
							lowerCamelCase__  =     int(self.size["shortest_edge"] * w / h     )
						else:
							lowerCamelCase__  =     self.size["shortest_edge"]
							lowerCamelCase__  =     self.size["shortest_edge"]

					else:
						lowerCamelCase__  =     []
						for image in image_inputs:
							lowerCamelCase__  ,	lowerCamelCase__  =     self.get_expected_values([image]     )
							expected_values.append((expected_height, expected_width)     )
						lowerCamelCase__  =     max(UpperCAmelCase_				,     key=lambda __lowerCamelCase     : item[0]     )[0]
						lowerCamelCase__  =     max(UpperCAmelCase_				,     key=lambda __lowerCamelCase     : item[1]     )[1]

					return expected_height, expected_width
@require_torch
@require_vision
class 				lowercase       (							__UpperCAmelCase ,       unittest.TestCase      ):


				'''simple docstring'''

				lowerCAmelCase__      				=							ConditionalDetrImageProcessor if is_vision_available() else None

				def    a__				(					self     :     Tuple     )							->							List[str]:

					'''simple docstring'''




					lowerCamelCase__  =     ConditionalDetrImageProcessingTester(self     )

				@property
				def    a__				(					self     :     Optional[Any]     )							->							Any:

					'''simple docstring'''




					return self.image_processor_tester.prepare_image_processor_dict()

				def    a__				(					self     :     Any     )							->							Tuple:

					'''simple docstring'''




					lowerCamelCase__  =     self.image_processing_class(**self.image_processor_dict     )
					self.assertTrue(hasattr(UpperCAmelCase_				,     "image_mean"     )     )
					self.assertTrue(hasattr(UpperCAmelCase_				,     "image_std"     )     )
					self.assertTrue(hasattr(UpperCAmelCase_				,     "do_normalize"     )     )
					self.assertTrue(hasattr(UpperCAmelCase_				,     "do_resize"     )     )
					self.assertTrue(hasattr(UpperCAmelCase_				,     "size"     )     )

				def    a__				(					self     :     List[Any]     )							->							Union[str, Any]:

					'''simple docstring'''




					lowerCamelCase__  =     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				,     UpperCAmelCase_     )

					lowerCamelCase__  =     self.image_processing_class.from_dict(
					    self.image_processor_dict				,     size=42				,     max_size=84				,     pad_and_return_pixel_mask=UpperCAmelCase_     )
					self.assertEqual(image_processor.size				,     {"shortest_edge": 42, "longest_edge": 84}     )
					self.assertEqual(image_processor.do_pad				,     UpperCAmelCase_     )

				def    a__				(					self     :     Optional[int]     )							->							Any:

					'''simple docstring'''




					pass

				def    a__				(					self     :     int     )							->							List[str]:

					'''simple docstring'''




					lowerCamelCase__  =     self.image_processing_class(**self.image_processor_dict     )
					# create random PIL images
					lowerCamelCase__  =     prepare_image_inputs(self.image_processor_tester				,     equal_resolution=UpperCAmelCase_     )
					for image in image_inputs:
						self.assertIsInstance(UpperCAmelCase_				,     Image.Image     )

					# Test not batched input
					lowerCamelCase__  =     image_processing(image_inputs[0]				,     return_tensors="pt"     ).pixel_values

					lowerCamelCase__  ,	lowerCamelCase__  =     self.image_processor_tester.get_expected_values(UpperCAmelCase_     )

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

					# Test batched
					lowerCamelCase__  ,	lowerCamelCase__  =     self.image_processor_tester.get_expected_values(UpperCAmelCase_				,     batched=UpperCAmelCase_     )

					lowerCamelCase__  =     image_processing(UpperCAmelCase_				,     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    a__				(					self     :     Optional[Any]     )							->							str:

					'''simple docstring'''




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

					# Test not batched input
					lowerCamelCase__  =     image_processing(image_inputs[0]				,     return_tensors="pt"     ).pixel_values

					lowerCamelCase__  ,	lowerCamelCase__  =     self.image_processor_tester.get_expected_values(UpperCAmelCase_     )

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

					# Test batched
					lowerCamelCase__  =     image_processing(UpperCAmelCase_				,     return_tensors="pt"     ).pixel_values

					lowerCamelCase__  ,	lowerCamelCase__  =     self.image_processor_tester.get_expected_values(UpperCAmelCase_				,     batched=UpperCAmelCase_     )

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

				def    a__				(					self     :     Tuple     )							->							Union[str, Any]:

					'''simple docstring'''




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

					# Test not batched input
					lowerCamelCase__  =     image_processing(image_inputs[0]				,     return_tensors="pt"     ).pixel_values

					lowerCamelCase__  ,	lowerCamelCase__  =     self.image_processor_tester.get_expected_values(UpperCAmelCase_     )

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

					# Test batched
					lowerCamelCase__  =     image_processing(UpperCAmelCase_				,     return_tensors="pt"     ).pixel_values

					lowerCamelCase__  ,	lowerCamelCase__  =     self.image_processor_tester.get_expected_values(UpperCAmelCase_				,     batched=UpperCAmelCase_     )

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

				@slow
				def    a__				(					self     :     str     )							->							Optional[Any]:

					'''simple docstring'''




					lowerCamelCase__  =     Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"     )
					with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"				,     "r"     ) as f:
						lowerCamelCase__  =     json.loads(f.read()     )

					lowerCamelCase__  =     {"image_id": 39769, "annotations": target}

					# encode them
					lowerCamelCase__  =     ConditionalDetrImageProcessor.from_pretrained("microsoft/conditional-detr-resnet-50"     )
					lowerCamelCase__  =     image_processing(images=UpperCAmelCase_				,     annotations=UpperCAmelCase_				,     return_tensors="pt"     )

					# verify pixel values
					lowerCamelCase__  =     torch.Size([1, 3, 800, 1066]     )
					self.assertEqual(encoding["pixel_values"].shape				,     UpperCAmelCase_     )

					lowerCamelCase__  =     torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]     )
					self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]				,     UpperCAmelCase_				,     atol=1E-4     )     )

					# verify area
					lowerCamelCase__  =     torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8]     )
					self.assertTrue(torch.allclose(encoding["labels"][0]["area"]				,     UpperCAmelCase_     )     )
					# verify boxes
					lowerCamelCase__  =     torch.Size([6, 4]     )
					self.assertEqual(encoding["labels"][0]["boxes"].shape				,     UpperCAmelCase_     )
					lowerCamelCase__  =     torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5]     )
					self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]				,     UpperCAmelCase_				,     atol=1E-3     )     )
					# verify image_id
					lowerCamelCase__  =     torch.tensor([39769]     )
					self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]				,     UpperCAmelCase_     )     )
					# verify is_crowd
					lowerCamelCase__  =     torch.tensor([0, 0, 0, 0, 0, 0]     )
					self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]				,     UpperCAmelCase_     )     )
					# verify class_labels
					lowerCamelCase__  =     torch.tensor([75, 75, 63, 65, 17, 17]     )
					self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]				,     UpperCAmelCase_     )     )
					# verify orig_size
					lowerCamelCase__  =     torch.tensor([480, 640]     )
					self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]				,     UpperCAmelCase_     )     )
					# verify size
					lowerCamelCase__  =     torch.tensor([800, 1066]     )
					self.assertTrue(torch.allclose(encoding["labels"][0]["size"]				,     UpperCAmelCase_     )     )




				@slow
				def    a__				(					self     :     str     )							->							Optional[Any]:

					'''simple docstring'''




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

					lowerCamelCase__  =     {"file_name": "000000039769.png", "image_id": 39769, "segments_info": target}

					lowerCamelCase__  =     pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic"     )

					# encode them
					lowerCamelCase__  =     ConditionalDetrImageProcessor(format="coco_panoptic"     )
					lowerCamelCase__  =     image_processing(images=UpperCAmelCase_				,     annotations=UpperCAmelCase_				,     masks_path=UpperCAmelCase_				,     return_tensors="pt"     )

					# verify pixel values
					lowerCamelCase__  =     torch.Size([1, 3, 800, 1066]     )
					self.assertEqual(encoding["pixel_values"].shape				,     UpperCAmelCase_     )

					lowerCamelCase__  =     torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]     )
					self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]				,     UpperCAmelCase_				,     atol=1E-4     )     )

					# verify area
					lowerCamelCase__  =     torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7]     )
					self.assertTrue(torch.allclose(encoding["labels"][0]["area"]				,     UpperCAmelCase_     )     )
					# verify boxes
					lowerCamelCase__  =     torch.Size([6, 4]     )
					self.assertEqual(encoding["labels"][0]["boxes"].shape				,     UpperCAmelCase_     )
					lowerCamelCase__  =     torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5]     )
					self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]				,     UpperCAmelCase_				,     atol=1E-3     )     )
					# verify image_id
					lowerCamelCase__  =     torch.tensor([39769]     )
					self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]				,     UpperCAmelCase_     )     )
					# verify is_crowd
					lowerCamelCase__  =     torch.tensor([0, 0, 0, 0, 0, 0]     )
					self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]				,     UpperCAmelCase_     )     )
					# verify class_labels
					lowerCamelCase__  =     torch.tensor([17, 17, 63, 75, 75, 93]     )
					self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]				,     UpperCAmelCase_     )     )
					# verify masks
					lowerCamelCase__  =     822873
					self.assertEqual(encoding["labels"][0]["masks"].sum().item()				,     UpperCAmelCase_     )
					# verify orig_size
					lowerCamelCase__  =     torch.tensor([480, 640]     )
					self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]				,     UpperCAmelCase_     )     )
					# verify size
					lowerCamelCase__  =     torch.tensor([800, 1066]     )
					self.assertTrue(torch.allclose(encoding["labels"][0]["size"]				,     UpperCAmelCase_     )     )









'''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 				lowercase       :


    '''simple docstring'''

    lowerCAmelCase__      				=							BlenderbotSmallConfig
    lowerCAmelCase__      				=							{}
    lowerCAmelCase__      				=							"gelu"

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

     '''simple docstring'''




     lowerCamelCase__  =     parent
     lowerCamelCase__  =     batch_size
     lowerCamelCase__  =     seq_length
     lowerCamelCase__  =     is_training
     lowerCamelCase__  =     use_labels
     lowerCamelCase__  =     vocab_size
     lowerCamelCase__  =     hidden_size
     lowerCamelCase__  =     num_hidden_layers
     lowerCamelCase__  =     num_attention_heads
     lowerCamelCase__  =     intermediate_size

     lowerCamelCase__  =     hidden_dropout_prob
     lowerCamelCase__  =     attention_probs_dropout_prob
     lowerCamelCase__  =     max_position_embeddings
     lowerCamelCase__  =     eos_token_id
     lowerCamelCase__  =     pad_token_id
     lowerCamelCase__  =     bos_token_id

    def    a__				(					self     :     str     )							->							List[str]:

     '''simple docstring'''




     lowerCamelCase__  =     ids_tensor([self.batch_size, self.seq_length - 1]				,     self.vocab_size     )
     lowerCamelCase__  =     tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size     )				,     1     )
     lowerCamelCase__  =     tf.concat([input_ids, eos_tensor]				,     axis=1     )

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

     lowerCamelCase__  =     self.config_cls(
         vocab_size=self.vocab_size				,     d_model=self.hidden_size				,     encoder_layers=self.num_hidden_layers				,     decoder_layers=self.num_hidden_layers				,     encoder_attention_heads=self.num_attention_heads				,     decoder_attention_heads=self.num_attention_heads				,     encoder_ffn_dim=self.intermediate_size				,     decoder_ffn_dim=self.intermediate_size				,     dropout=self.hidden_dropout_prob				,     attention_dropout=self.attention_probs_dropout_prob				,     max_position_embeddings=self.max_position_embeddings				,     eos_token_ids=[2]				,     bos_token_id=self.bos_token_id				,     pad_token_id=self.pad_token_id				,     decoder_start_token_id=self.pad_token_id				,     **self.config_updates				,     )
     lowerCamelCase__  =     prepare_blenderbot_small_inputs_dict(__lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase     )
     return config, inputs_dict




    def    a__				(					self     :     int				,     __lowerCamelCase     :     Optional[int]				,     __lowerCamelCase     :     Optional[Any]     )							->							Tuple:

     '''simple docstring'''




     lowerCamelCase__  =     TFBlenderbotSmallModel(config=__lowerCamelCase     ).get_decoder()
     lowerCamelCase__  =     inputs_dict["input_ids"]

     lowerCamelCase__  =     input_ids[:1, :]
     lowerCamelCase__  =     inputs_dict["attention_mask"][:1, :]
     lowerCamelCase__  =     inputs_dict["head_mask"]
     lowerCamelCase__  =     1

     # first forward pass
     lowerCamelCase__  =     model(__lowerCamelCase				,     attention_mask=__lowerCamelCase				,     head_mask=__lowerCamelCase				,     use_cache=__lowerCamelCase     )

     lowerCamelCase__  ,	lowerCamelCase__  =     outputs.to_tuple()

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

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

     lowerCamelCase__  =     model(__lowerCamelCase				,     attention_mask=__lowerCamelCase     )[0]
     lowerCamelCase__  =     model(__lowerCamelCase				,     attention_mask=__lowerCamelCase				,     past_key_values=__lowerCamelCase     )[0]

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

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

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






def    lowerCamelCase_     (    lowercase__				,     lowercase__				,     lowercase__				,     lowercase__=None				,     lowercase__=None				,     lowercase__=None				,     lowercase__=None				,     lowercase__=None				,     ):
 if attention_mask is None:
  lowerCamelCase__  =     tf.cast(tf.math.not_equal(lowercase__				,     config.pad_token_id)				,     tf.inta)
 if decoder_attention_mask is None:
  lowerCamelCase__  =     tf.concat(
      [
          tf.ones(decoder_input_ids[:, :1].shape				,     dtype=tf.inta),
          tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]				,     config.pad_token_id)				,     tf.inta),
      ]				,     axis=-1				,     )
 if head_mask is None:
  lowerCamelCase__  =     tf.ones((config.encoder_layers, config.encoder_attention_heads))
 if decoder_head_mask is None:
  lowerCamelCase__  =     tf.ones((config.decoder_layers, config.decoder_attention_heads))
 if cross_attn_head_mask is None:
  lowerCamelCase__  =     tf.ones((config.decoder_layers, config.decoder_attention_heads))
 return {
     "input_ids": input_ids,
     "decoder_input_ids": decoder_input_ids,
     "attention_mask": attention_mask,
     "decoder_attention_mask": decoder_attention_mask,
     "head_mask": head_mask,
     "decoder_head_mask": decoder_head_mask,
     "cross_attn_head_mask": cross_attn_head_mask,
 }
@require_tf
class 				lowercase       (							_lowerCamelCase ,       _lowerCamelCase ,       unittest.TestCase      ):


    '''simple docstring'''

    lowerCAmelCase__      				=							(
        (TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel) if is_tf_available() else ()
    )
    lowerCAmelCase__      				=							(TFBlenderbotSmallForConditionalGeneration,) if is_tf_available() else ()
    lowerCAmelCase__      				=							(
        {
            "conversational": TFBlenderbotSmallForConditionalGeneration,
            "feature-extraction": TFBlenderbotSmallModel,
            "summarization": TFBlenderbotSmallForConditionalGeneration,
            "text2text-generation": TFBlenderbotSmallForConditionalGeneration,
            "translation": TFBlenderbotSmallForConditionalGeneration,
        }
        if is_tf_available()
        else {}
    )
    lowerCAmelCase__      				=							True
    lowerCAmelCase__      				=							False
    lowerCAmelCase__      				=							False

    def    a__				(					self     :     List[Any]     )							->							Optional[Any]:

     '''simple docstring'''




     lowerCamelCase__  =     TFBlenderbotSmallModelTester(self     )
     lowerCamelCase__  =     ConfigTester(self				,     config_class=__lowerCamelCase     )

    def    a__				(					self     :     Any     )							->							Tuple:

     '''simple docstring'''




     self.config_tester.run_common_tests()




    def    a__				(					self     :     List[Any]     )							->							List[str]:

     '''simple docstring'''




     lowerCamelCase__  =     self.model_tester.prepare_config_and_inputs_for_common()
     self.model_tester.check_decoder_model_past_large_inputs(*__lowerCamelCase     )
@require_tokenizers
@require_tf
class 				lowercase       (							unittest.TestCase      ):


    '''simple docstring'''

    lowerCAmelCase__      				=							[
        "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?"
    ]
    lowerCAmelCase__      				=							"facebook/blenderbot_small-90M"

    @cached_property
    def    a__				(					self     :     Optional[Any]     )							->							Tuple:

     '''simple docstring'''




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

    @cached_property
    def    a__				(					self     :     int     )							->							int:

     '''simple docstring'''




     lowerCamelCase__  =     TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name     )
     return model




    @slow
    def    a__				(					self     :     Any     )							->							Union[str, Any]:

     '''simple docstring'''




     lowerCamelCase__  =     self.tokenizer(self.src_text				,     return_tensors="tf"     )
     lowerCamelCase__  =     self.model.generate(
         model_inputs.input_ids				,     attention_mask=model_inputs.attention_mask				,     num_beams=2				,     use_cache=__lowerCamelCase				,     )
     lowerCamelCase__  =     self.tokenizer.batch_decode(generated_ids.numpy()				,     skip_special_tokens=__lowerCamelCase     )[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.",
     )

