






'''simple docstring'''




import json
import os
import unittest

from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin







class       __lowercase					(UpperCAmelCase__   ,				unittest.TestCase    ):
 _lowerCamelCase              =  CTRLTokenizer
 _lowerCamelCase              =  False
 _lowerCamelCase              =  False

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

   # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
   UpperCamelCase__				:					str					=					["adapt", "re@@", "a@@", "apt", "c@@", "t", "<unk>"]
   UpperCamelCase__				:					List[Any]					=					dict(zip(lowerCamelCase__  ,					range(len(lowerCamelCase__))))
   UpperCamelCase__				:					Dict					=					["#version: 0.2", "a p", "ap t</w>", "r e", "a d", "ad apt</w>", ""]
   UpperCamelCase__				:					Any					=					{"unk_token": "<unk>"}

   UpperCamelCase__				:					Any					=					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(lowerCamelCase__) + '\n')
   with open(self.merges_file  ,					'w'  ,					encoding='utf-8') as fp:
     fp.write('\n'.join(lowerCamelCase__))

 def      __UpperCamelCase							(      self   :						Optional[int]  ,					**UpperCAmelCase_   :						Optional[int]):
   kwargs.update(self.special_tokens_map)
   return CTRLTokenizer.from_pretrained(self.tmpdirname  ,					**lowerCamelCase__)

 def      __UpperCamelCase							(      self   :						List[Any]  ,					UpperCAmelCase_   :						Optional[Any]):
   UpperCamelCase__				:					int					=					"adapt react readapt apt"
   UpperCamelCase__				:					Optional[int]					=					"adapt react readapt apt"
   return input_text, output_text





 def      __UpperCamelCase							(      self   :						int):
   UpperCamelCase__				:					List[Any]					=					CTRLTokenizer(self.vocab_file  ,					self.merges_file  ,					**self.special_tokens_map)
   UpperCamelCase__				:					int					=					"adapt react readapt apt"
   UpperCamelCase__				:					List[str]					=					"adapt re@@ a@@ c@@ t re@@ adapt apt".split()
   UpperCamelCase__				:					str					=					tokenizer.tokenize(lowerCamelCase__)
   self.assertListEqual(lowerCamelCase__  ,					lowerCamelCase__)

   UpperCamelCase__				:					Tuple					=					tokens + [tokenizer.unk_token]

   UpperCamelCase__				:					Optional[int]					=					[0, 1, 2, 4, 5, 1, 0, 3, 6]
   self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase__)  ,					lowerCamelCase__)












'''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))




