"""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 						__A							(			SCREAMING_SNAKE_CASE_   ,     unittest.TestCase		):
     _UpperCamelCase							:      Dict		 =		BlenderbotSmallTokenizer
     _UpperCamelCase							:      Tuple		 =		False






     def    __A  (		self  ):
       super().setUp()

       _lowerCAmelCase						:	Any        =					["""__start__""", """adapt""", """act""", """ap@@""", """te""", """__end__""", """__unk__"""]
       _lowerCAmelCase						:	Tuple        =					dict(zip(a__    ,					range(len(a__  )  )  )  )

       _lowerCAmelCase						:	Union[str, Any]        =					["""#version: 0.2""", """a p""", """t e</w>""", """ap t</w>""", """a d""", """ad apt</w>""", """a c""", """ac t</w>""", """"""]
       _lowerCAmelCase						:	List[str]        =					{"""unk_token""": """__unk__""", """bos_token""": """__start__""", """eos_token""": """__end__"""}

       _lowerCAmelCase						:	Any        =					os.path.join(self.tmpdirname    ,					VOCAB_FILES_NAMES["""vocab_file"""]  )
       _lowerCAmelCase						:	Optional[int]        =					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    __A  (		self    ,					**a__  ):
       kwargs.update(self.special_tokens_map  )
       return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname    ,					**a__  )






     def    __A  (		self    ,					a__  ):
       _lowerCAmelCase						:	List[Any]        =					"""adapt act apte"""
       _lowerCAmelCase						:	int        =					"""adapt act apte"""
       return input_text, output_text






     def    __A  (		self  ):
       _lowerCAmelCase						:	int        =					BlenderbotSmallTokenizer(self.vocab_file    ,					self.merges_file    ,					**self.special_tokens_map  )
       _lowerCAmelCase						:	Dict        =					"""adapt act apte"""
       _lowerCAmelCase						:	Any        =					["""adapt""", """act""", """ap@@""", """te"""]
       _lowerCAmelCase						:	str        =					tokenizer.tokenize(a__  )
       self.assertListEqual(a__    ,					a__  )

       _lowerCAmelCase						:	Optional[int]        =					[tokenizer.bos_token] + tokens + [tokenizer.eos_token]

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






     def    __A  (		self  ):
       _lowerCAmelCase						:	str        =					BlenderbotSmallTokenizer.from_pretrained("""facebook/blenderbot-90M"""  )
       assert tok("""sam"""  ).input_ids == [1384]
       _lowerCAmelCase						:	List[str]        =					"""I am a small frog."""
       _lowerCAmelCase						:	Dict        =					tok([src_text]    ,					padding=a__    ,					truncation=a__  )["""input_ids"""]
       _lowerCAmelCase						:	Optional[Any]        =					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    __A  (		self  ):
       _lowerCAmelCase						:	Optional[int]        =					BlenderbotSmallTokenizer.from_pretrained("""facebook/blenderbot-90M"""  )
       _lowerCAmelCase						:	Any        =					"""I am a small frog ."""
       _lowerCAmelCase						:	Any        =					"""."""
       _lowerCAmelCase						:	List[str]        =					tok(a__  )["""input_ids"""]
       _lowerCAmelCase						:	Any        =					tok(a__  )["""input_ids"""]

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





"""simple docstring"""




import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
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 PoolFormerImageProcessor
class 						__A							(			unittest.TestCase		):






     def __init__(		self    ,					a__    ,					a__=7    ,					a__=3    ,					a__=30    ,					a__=400    ,					a__=True    ,					a__=None    ,					a__=0.9    ,					a__=None    ,					a__=True    ,					a__=[0.5, 0.5, 0.5]    ,					a__=[0.5, 0.5, 0.5]    ,					):
       _lowerCAmelCase						:	int        =					size if size is not None else {"""shortest_edge""": 30}
       _lowerCAmelCase						:	Dict        =					crop_size if crop_size is not None else {"""height""": 30, """width""": 30}
       _lowerCAmelCase						:	List[Any]        =					parent
       _lowerCAmelCase						:	Optional[int]        =					batch_size
       _lowerCAmelCase						:	Any        =					num_channels
       _lowerCAmelCase						:	str        =					min_resolution
       _lowerCAmelCase						:	Dict        =					max_resolution
       _lowerCAmelCase						:	str        =					do_resize_and_center_crop
       _lowerCAmelCase						:	List[str]        =					size
       _lowerCAmelCase						:	int        =					crop_pct
       _lowerCAmelCase						:	int        =					crop_size
       _lowerCAmelCase						:	Union[str, Any]        =					do_normalize
       _lowerCAmelCase						:	Tuple        =					image_mean
       _lowerCAmelCase						:	Optional[Any]        =					image_std






     def    __A  (		self  ):
       return {
           "size": self.size,
           "do_resize_and_center_crop": self.do_resize_and_center_crop,
           "crop_pct": self.crop_pct,
           "crop_size": self.crop_size,
           "do_normalize": self.do_normalize,
           "image_mean": self.image_mean,
           "image_std": self.image_std,
       }







@require_torch
@require_vision
class 						__A							(			SCREAMING_SNAKE_CASE_   ,     unittest.TestCase		):
     _UpperCamelCase							:      List[str]		 =		PoolFormerImageProcessor if is_vision_available() else None






     def    __A  (		self  ):
       _lowerCAmelCase						:	str        =					PoolFormerImageProcessingTester(self  )






     @property
     def    __A  (		self  ):
       return self.image_processor_tester.prepare_image_processor_dict()






     def    __A  (		self  ):
       _lowerCAmelCase						:	List[str]        =					self.image_processing_class(**self.image_processor_dict  )
       self.assertTrue(hasattr(a__    ,					"""do_resize_and_center_crop"""  )  )
       self.assertTrue(hasattr(a__    ,					"""size"""  )  )
       self.assertTrue(hasattr(a__    ,					"""crop_pct"""  )  )
       self.assertTrue(hasattr(a__    ,					"""do_normalize"""  )  )
       self.assertTrue(hasattr(a__    ,					"""image_mean"""  )  )
       self.assertTrue(hasattr(a__    ,					"""image_std"""  )  )






     def    __A  (		self  ):
       _lowerCAmelCase						:	List[str]        =					self.image_processing_class.from_dict(self.image_processor_dict  )
       self.assertEqual(image_processor.size    ,					{"""shortest_edge""": 30}  )
       self.assertEqual(image_processor.crop_size    ,					{"""height""": 30, """width""": 30}  )

       _lowerCAmelCase						:	List[Any]        =					self.image_processing_class.from_dict(self.image_processor_dict    ,					size=42    ,					crop_size=84  )
       self.assertEqual(image_processor.size    ,					{"""shortest_edge""": 42}  )
       self.assertEqual(image_processor.crop_size    ,					{"""height""": 84, """width""": 84}  )






     def    __A  (		self  ):
       pass






     def    __A  (		self  ):
       # Initialize image_processing
       _lowerCAmelCase						:	List[str]        =					self.image_processing_class(**self.image_processor_dict  )
       # create random PIL images
       _lowerCAmelCase						:	Tuple        =					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						:	Tuple        =					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.crop_size["""height"""],
               self.image_processor_tester.crop_size["""width"""],
           )    ,					)

       # Test batched
       _lowerCAmelCase						:	List[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.crop_size["""height"""],
               self.image_processor_tester.crop_size["""width"""],
           )    ,					)






     def    __A  (		self  ):
       # Initialize image_processing
       _lowerCAmelCase						:	Union[str, Any]        =					self.image_processing_class(**self.image_processor_dict  )
       # create random numpy tensors
       _lowerCAmelCase						:	Union[str, 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						:	Dict        =					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.crop_size["""height"""],
               self.image_processor_tester.crop_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.crop_size["""height"""],
               self.image_processor_tester.crop_size["""width"""],
           )    ,					)






     def    __A  (		self  ):
       # Initialize image_processing
       _lowerCAmelCase						:	Any        =					self.image_processing_class(**self.image_processor_dict  )
       # create random PyTorch tensors
       _lowerCAmelCase						:	Any        =					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						:	str        =					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.crop_size["""height"""],
               self.image_processor_tester.crop_size["""width"""],
           )    ,					)

       # Test batched
       _lowerCAmelCase						:	List[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.crop_size["""height"""],
               self.image_processor_tester.crop_size["""width"""],
           )    ,					)




