






'''simple docstring'''




import json
import os
import unittest

from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
    VOCAB_FILES_NAMES,
    BlenderbotSmallTokenizer,
)

from ...test_tokenization_common import TokenizerTesterMixin







class       __lowercase					(__lowerCamelCase   ,				unittest.TestCase    ):
 _lowerCamelCase              =  BlenderbotSmallTokenizer
 _lowerCamelCase              =  False

 def      __UpperCamelCase							(      self   :						List[str]):
   super().setUp()

   UpperCamelCase__				:					Union[str, Any]					=					['__start__', 'adapt', 'act', 'ap@@', 'te', '__end__', '__unk__']
   UpperCamelCase__				:					List[Any]					=					dict(zip(__A  ,					range(len(__A))))

   UpperCamelCase__				:					Dict					=					['#version: 0.2', 'a p', 't e</w>', 'ap t</w>', 'a d', 'ad apt</w>', 'a c', 'ac t</w>', '']
   UpperCamelCase__				:					str					=					{'unk_token': '__unk__', 'bos_token': '__start__', 'eos_token': '__end__'}

   UpperCamelCase__				:					Optional[int]					=					os.path.join(self.tmpdirname  ,					VOCAB_FILES_NAMES['vocab_file'])
   UpperCamelCase__				:					Optional[Any]					=					os.path.join(self.tmpdirname  ,					VOCAB_FILES_NAMES['merges_file'])
   with open(self.vocab_file  ,					'w'  ,					encoding='utf-8') as fp:
     fp.write(json.dumps(__A) + '\n')
   with open(self.merges_file  ,					'w'  ,					encoding='utf-8') as fp:
     fp.write('\n'.join(__A))

 def      __UpperCamelCase							(      self   :						str  ,					**UpperCAmelCase_   :						Optional[Any]):
   kwargs.update(self.special_tokens_map)
   return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname  ,					**__A)

 def      __UpperCamelCase							(      self   :						str  ,					UpperCAmelCase_   :						List[str]):
   UpperCamelCase__				:					Tuple					=					'adapt act apte'
   UpperCamelCase__				:					Tuple					=					'adapt act apte'
   return input_text, output_text

 def      __UpperCamelCase							(      self   :						Union[str, Any]):
   UpperCamelCase__				:					Optional[Any]					=					BlenderbotSmallTokenizer(self.vocab_file  ,					self.merges_file  ,					**self.special_tokens_map)
   UpperCamelCase__				:					int					=					'adapt act apte'
   UpperCamelCase__				:					str					=					['adapt', 'act', 'ap@@', 'te']
   UpperCamelCase__				:					Optional[int]					=					tokenizer.tokenize(__A)
   self.assertListEqual(__A  ,					__A)

   UpperCamelCase__				:					Tuple					=					[tokenizer.bos_token] + tokens + [tokenizer.eos_token]

   UpperCamelCase__				:					List[Any]					=					[0, 1, 2, 3, 4, 5]
   self.assertListEqual(tokenizer.convert_tokens_to_ids(__A)  ,					__A)

 def      __UpperCamelCase							(      self   :						Any):
   UpperCamelCase__				:					int					=					BlenderbotSmallTokenizer.from_pretrained('facebook/blenderbot-90M')
   assert tok('sam').input_ids == [1_384]
   UpperCamelCase__				:					Union[str, Any]					=					'I am a small frog.'
   UpperCamelCase__				:					Tuple					=					tok([src_text]  ,					padding=__A  ,					truncation=__A)['input_ids']
   UpperCamelCase__				:					int					=					tok.batch_decode(__A  ,					skip_special_tokens=__A  ,					clean_up_tokenization_spaces=__A)[0]
   assert src_text != decoded  # I wish it did!
   assert decoded == "i am a small frog ."





 def      __UpperCamelCase							(      self   :						Any):
   UpperCamelCase__				:					Tuple					=					BlenderbotSmallTokenizer.from_pretrained('facebook/blenderbot-90M')
   UpperCamelCase__				:					Dict					=					'I am a small frog .'
   UpperCamelCase__				:					str					=					'.'
   UpperCamelCase__				:					Optional[int]					=					tok(__A)['input_ids']
   UpperCamelCase__				:					List[Any]					=					tok(__A)['input_ids']

   assert encoded[-1] == encoded_dot[0]












'''simple docstring'''




import unittest

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device


if is_torch_available():
		import torch

		from transformers import AutoModelForImageClassification

if is_vision_available():
		from transformers import AutoImageProcessor







@require_torch
@require_vision
class       __lowercase					(unittest.TestCase    ):

	@slow
	def      __UpperCamelCase							(      self   :						int):
			UpperCamelCase__				:					Union[str, Any]					=					AutoImageProcessor.from_pretrained('microsoft/dit-base-finetuned-rvlcdip')
			UpperCamelCase__				:					List[str]					=					AutoModelForImageClassification.from_pretrained('microsoft/dit-base-finetuned-rvlcdip')
			model.to(UpperCAmelCase_)

			from datasets import load_dataset

			UpperCamelCase__				:					Optional[Any]					=					load_dataset('nielsr/rvlcdip-demo')

			UpperCamelCase__				:					int					=					dataset['train'][0]['image'].convert('RGB')

			UpperCamelCase__				:					Union[str, Any]					=					image_processor(UpperCAmelCase_  ,					return_tensors='pt').to(UpperCAmelCase_)

			# forward pass
			with torch.no_grad():
					UpperCamelCase__				:					Optional[Any]					=					model(**UpperCAmelCase_)
					UpperCamelCase__				:					Tuple					=					outputs.logits

			UpperCamelCase__				:					str					=					torch.Size((1, 16))
			self.assertEqual(logits.shape  ,					UpperCAmelCase_)

			UpperCamelCase__				:					Tuple					=					torch.tensor(
			    [-0.41_58, -0.40_92, -0.43_47]  ,					device=UpperCAmelCase_  ,					dtype=torch.float  ,					)
			self.assertTrue(torch.allclose(logits[0, :3]  ,					UpperCAmelCase_  ,					atol=1e-4))




