



import unittest

import numpy as np
import PIL
import torch

from diffusers.image_processor import VaeImageProcessor



class       _lowercase				(  unittest.TestCase      ):





	"""simple docstring"""




	@property
	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						a   	=  1
						a   	=  3
						a   	=  8
						a   	=  8

						a   	=  torch.rand((batch_size, num_channels, height, width)						)

						return sample




	def 	UpperCamelCase_			(self						,			lowerCamelCase_						):
						"""simple docstring"""

						if isinstance(image[0]						,			PIL.Image.Image						):
											return np.stack([np.array(lowerCAmelCase_						) for i in image]						,			axis=0						)
						elif isinstance(lowerCAmelCase_						,			torch.Tensor						):
											return image.cpu().numpy().transpose(0						,			2						,			3						,			1						)
						return image




	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						a   	=  VaeImageProcessor(do_resize=lowerCAmelCase_						,			do_normalize=lowerCAmelCase_						)

						a   	=  self.dummy_sample
						a   	=  self.to_np(lowerCAmelCase_						)

						for output_type in ["pt", "np", "pil"]:
											a   	=  image_processor.postprocess(
											    image_processor.preprocess(lowerCAmelCase_						)						,			output_type=lowerCAmelCase_						,			)
											a   	=  self.to_np(lowerCAmelCase_						)
											a   	=  (input_np * 255).round() if output_type == '''pil''' else input_np
											assert (
											    np.abs(in_np - out_np						).max() < 1E-6
											), F'''decoded output does not match input for output_type {output_type}'''




	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						a   	=  VaeImageProcessor(do_resize=lowerCAmelCase_						,			do_normalize=lowerCAmelCase_						)
						a   	=  self.dummy_sample.cpu().numpy().transpose(0						,			2						,			3						,			1						)

						for output_type in ["pt", "np", "pil"]:
											a   	=  image_processor.postprocess(image_processor.preprocess(lowerCAmelCase_						)						,			output_type=lowerCAmelCase_						)

											a   	=  self.to_np(lowerCAmelCase_						)
											a   	=  (input_np * 255).round() if output_type == '''pil''' else input_np
											assert (
											    np.abs(in_np - out_np						).max() < 1E-6
											), F'''decoded output does not match input for output_type {output_type}'''




	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						a   	=  VaeImageProcessor(do_resize=lowerCAmelCase_						,			do_normalize=lowerCAmelCase_						)

						a   	=  self.dummy_sample.cpu().numpy().transpose(0						,			2						,			3						,			1						)
						a   	=  image_processor.numpy_to_pil(lowerCAmelCase_						)

						for output_type in ["pt", "np", "pil"]:
											a   	=  image_processor.postprocess(image_processor.preprocess(lowerCAmelCase_						)						,			output_type=lowerCAmelCase_						)
											for i, o in zip(lowerCAmelCase_						,			lowerCAmelCase_						):
																a   	=  np.array(lowerCAmelCase_						)
																a   	=  self.to_np(lowerCAmelCase_						) if output_type == '''pil''' else (self.to_np(lowerCAmelCase_						) * 255).round()
																assert (
																    np.abs(in_np - out_np						).max() < 1E-6
																), F'''decoded output does not match input for output_type {output_type}'''




	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						a   	=  VaeImageProcessor(do_resize=lowerCAmelCase_						,			do_normalize=lowerCAmelCase_						)

						a   	=  self.dummy_sample
						a   	=  input_pt_ad.squeeze(0						)

						a   	=  image_processor.postprocess(
						    image_processor.preprocess(lowerCAmelCase_						)						,			output_type="np"						,			)
						a   	=  image_processor.postprocess(
						    image_processor.preprocess(lowerCAmelCase_						)						,			output_type="np"						,			)

						a   	=  self.to_np(self.dummy_sample						)
						a   	=  input_np_ad.squeeze(0						)

						a   	=  image_processor.postprocess(
						    image_processor.preprocess(lowerCAmelCase_						)						,			output_type="np"						,			)
						a   	=  image_processor.postprocess(
						    image_processor.preprocess(lowerCAmelCase_						)						,			output_type="np"						,			)

						assert np.abs(out_pt_ad - out_pt_ad						).max() < 1E-6
						assert np.abs(out_np_ad - out_np_ad						).max() < 1E-6




	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						a   	=  VaeImageProcessor(do_resize=lowerCAmelCase_						,			do_normalize=lowerCAmelCase_						)

						a   	=  self.dummy_sample
						a   	=  list(lowerCAmelCase_						)

						a   	=  image_processor.postprocess(
						    image_processor.preprocess(lowerCAmelCase_						)						,			output_type="np"						,			)

						a   	=  image_processor.postprocess(
						    image_processor.preprocess(lowerCAmelCase_						)						,			output_type="np"						,			)

						a   	=  self.to_np(self.dummy_sample						)
						list(lowerCAmelCase_						)

						a   	=  image_processor.postprocess(
						    image_processor.preprocess(lowerCAmelCase_						)						,			output_type="np"						,			)

						a   	=  image_processor.postprocess(
						    image_processor.preprocess(lowerCAmelCase_						)						,			output_type="np"						,			)

						assert np.abs(out_pt_ad - out_pt_list						).max() < 1E-6
						assert np.abs(out_np_ad - out_np_list						).max() < 1E-6

import os
import tempfile
import unittest
from typing import List

from transformers.models.esm.tokenization_esm import VOCAB_FILES_NAMES, EsmTokenizer
from transformers.testing_utils import require_tokenizers
from transformers.tokenization_utils import PreTrainedTokenizer
from transformers.tokenization_utils_base import PreTrainedTokenizerBase






@require_tokenizers
class 				_SCREAMING_SNAKE_CASE( unittest.TestCase						):
   UpperCamelCase_					:    int			      = EsmTokenizer
   def _lowercase(     self     )						->Dict:
        super().setUp()
        a	:     Tuple						      =    tempfile.mkdtemp()
        # fmt: off
        a	:     List[str]						      =    ["<cls>", "<pad>", "<eos>", "<unk>", "L", "A", "G", "V", "S", "E", "R", "T", "I", "D", "P", "K", "Q", "N", "F", "Y", "M", "H", "W", "C", "X", "B", "U", "Z", "O", ".", "-", "<null_1>", "<mask>"]  # noqa: E501
        # fmt: on
        a	:     Tuple						      =    os.path.join(self.tmpdirname				,  VOCAB_FILES_NAMES['''vocab_file''']     )
        with open(self.vocab_file				,  '''w'''				,  encoding='''utf-8'''     ) as vocab_writer:
             vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]     )     )
   def _lowercase(     self				,  **lowerCAmelCase_     )						->List[PreTrainedTokenizerBase]:
        return [self.get_tokenizer(**lowerCAmelCase_     )]
   def _lowercase(     self				,  **lowerCAmelCase_     )						->PreTrainedTokenizer:
        return self.tokenizer_class.from_pretrained(self.tmpdirname				,  **lowerCAmelCase_     )
   def _lowercase(     self     )						->List[Any]:
        a	:     Optional[int]						      =    self.tokenizer_class(self.vocab_file     )

        a	:     List[Any]						      =    tokenizer.tokenize('''LAGVS'''     )
        self.assertListEqual(lowerCAmelCase_				,  ['''L''', '''A''', '''G''', '''V''', '''S''']     )
        self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase_     )				,  [4, 5, 6, 7, 8]     )
   def _lowercase(     self     )						->Optional[Any]:
        a	:     Optional[int]						      =    self.tokenizer_class(self.vocab_file     )

        a	:     Optional[Any]						      =    '''LAGVS'''
        self.assertListEqual(tokenizer.encode(lowerCAmelCase_     )				,  [0, 4, 5, 6, 7, 8, 2]     )
   def _lowercase(     self     )						->List[Any]:
        a	:     Any						      =    self.tokenizer_class(self.vocab_file     )

        a	:     Optional[int]						      =    ['''LAGVS''', '''WCB''']
        a	:     Optional[Any]						      =    tokenizer(lowerCAmelCase_				,  padding=lowerCAmelCase_     )['''input_ids''']

        self.assertListEqual(lowerCAmelCase_				,  [[0, 4, 5, 6, 7, 8, 2], [0, 22, 23, 25, 2]]     )
   def _lowercase(     self     )						->List[Any]:
        a	:     Optional[Any]						      =    self.tokenizer_class(self.vocab_file     )

        a	:     str						      =    ['''LAGVS''', '''WCB''']
        a	:     Dict						      =    tokenizer(lowerCAmelCase_				,  padding=lowerCAmelCase_     )['''input_ids''']

        self.assertListEqual(lowerCAmelCase_				,  [[0, 4, 5, 6, 7, 8, 2], [0, 22, 23, 25, 2, 1, 1]]     )


   def _lowercase(     self     )						->List[str]:
        a	:     int						      =    self.get_tokenizers(fast=lowerCAmelCase_     )
        for tokenizer in tokenizers:
             with self.subTest(F"""{tokenizer.__class__.__name__}"""     ):
                  a	:     Union[str, Any]						      =    '''<unk>'''
                  a	:     List[Any]						      =    '''<mask>'''

                  a	:     List[Any]						      =    tokenizer.tokenize(lowerCAmelCase_     )
                  a	:     Tuple						      =    tokenizer.tokenize(lowerCAmelCase_     )

                  self.assertEqual(len(lowerCAmelCase_     )				,  1     )
                  self.assertEqual(len(lowerCAmelCase_     )				,  1     )
                  self.assertEqual(token_a[0]				,  lowerCAmelCase_     )
                  self.assertEqual(token_a[0]				,  lowerCAmelCase_     )