





import json
import os
import unittest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
class  __lowerCAmelCase	(      _a,					unittest.TestCase					):
			lowerCamelCase_					:					List[str]	      =				CLIPTokenizer
			lowerCamelCase_					:					Dict	      =				CLIPTokenizerFast
			lowerCamelCase_					:					str	      =				True
			lowerCamelCase_					:					str	      =				{}
			lowerCamelCase_					:					List[Any]	      =				False






			def       lowerCamelCase  (self   )					->       int:


							'''simple docstring'''



							super().setUp()

							# fmt: off
							snake_case_						:   Optional[int]           =  ['''l''', '''o''', '''w''', '''e''', '''r''', '''s''', '''t''', '''i''', '''d''', '''n''', '''lo''', '''l</w>''', '''w</w>''', '''r</w>''', '''t</w>''', '''low</w>''', '''er</w>''', '''lowest</w>''', '''newer</w>''', '''wider''', '''<unk>''', '''<|startoftext|>''', '''<|endoftext|>''']
							# fmt: on
							snake_case_						:   Union[str, Any]           =  dict(zip(__magic_name__ ,							range(len(__magic_name__   )   )   )   )
							snake_case_						:   Optional[int]           =  ['''#version: 0.2''', '''l o''', '''lo w</w>''', '''e r</w>''']
							snake_case_						:   Dict           =  {'''unk_token''': '''<unk>'''}

							snake_case_						:   Tuple           =  os.path.join(self.tmpdirname ,							VOCAB_FILES_NAMES['''vocab_file''']   )
							snake_case_						:   Tuple           =  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(__magic_name__   ) + '''\n'''   )
							with open(self.merges_file ,							'''w''' ,							encoding='''utf-8'''   ) as fp:
											fp.write('''\n'''.join(__magic_name__   )   )






			def       lowerCamelCase  (self ,							**__magic_name__   )					->       Dict:


							'''simple docstring'''



							kwargs.update(self.special_tokens_map   )
							return CLIPTokenizer.from_pretrained(self.tmpdirname ,							**__magic_name__   )






			def       lowerCamelCase  (self ,							**__magic_name__   )					->       Union[str, Any]:


							'''simple docstring'''



							kwargs.update(self.special_tokens_map   )
							return CLIPTokenizerFast.from_pretrained(self.tmpdirname ,							**__magic_name__   )






			def       lowerCamelCase  (self ,							__magic_name__   )					->       Any:


							'''simple docstring'''



							snake_case_						:   Dict           =  '''lower newer'''
							snake_case_						:   str           =  '''lower newer'''
							return input_text, output_text






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


							'''simple docstring'''



							snake_case_						:   List[str]           =  CLIPTokenizer(self.vocab_file ,							self.merges_file ,							**self.special_tokens_map   )
							snake_case_						:   Union[str, Any]           =  '''lower newer'''
							snake_case_						:   List[Any]           =  ['''lo''', '''w''', '''er</w>''', '''n''', '''e''', '''w''', '''er</w>''']
							snake_case_						:   List[str]           =  tokenizer.tokenize(__magic_name__   )
							self.assertListEqual(__magic_name__ ,							__magic_name__   )

							snake_case_						:   int           =  tokens + [tokenizer.unk_token]
							snake_case_						:   Dict           =  [10, 2, 16, 9, 3, 2, 16, 20]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(__magic_name__   ) ,							__magic_name__   )






			@require_ftfy
			def       lowerCamelCase  (self   )					->       Any:


							'''simple docstring'''



							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
											with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''   ):
															snake_case_						:   Union[str, Any]           =  self.tokenizer_class.from_pretrained(__magic_name__ ,							**__magic_name__   )
															snake_case_						:   str           =  self.rust_tokenizer_class.from_pretrained(__magic_name__ ,							**__magic_name__   )

															snake_case_						:   Union[str, Any]           =  '''A\n\'ll 11p223RF☆ho!!to?\'d\'d\'\'d of a cat to-$\'\'d.'''
															snake_case_						:   Tuple           =  tokenizer_s.tokenize(__magic_name__   )
															snake_case_						:   List[Any]           =  tokenizer_r.tokenize(__magic_name__   )

															self.assertListEqual(__magic_name__ ,							__magic_name__   )

															# Test that the tokenization is identical on an example containing a character (Latin Small Letter A
															# with Tilde) encoded in 2 different ways
															snake_case_						:   str           =  '''xa\u0303y''' + ''' ''' + '''x\xe3y'''
															snake_case_						:   Optional[int]           =  tokenizer_s.tokenize(__magic_name__   )
															snake_case_						:   str           =  tokenizer_r.tokenize(__magic_name__   )

															self.assertListEqual(__magic_name__ ,							__magic_name__   )

															# Test that the tokenization is identical on unicode of space type
															snake_case_						:   Union[str, Any]           =  [
															    '''\u0009''',  # (horizontal tab, '\t')
															    '''\u000B''',  # (vertical tab)
															    '''\u000C''',  # (form feed)
															    '''\u0020''',  # (space, ' ')
															    '''\u200E''',  # (left-to-right mark):w
															    '''\u200F''',  # (right-to-left mark)
															]
															for unicode_seq in spaces_unicodes:
																			snake_case_						:   Any           =  tokenizer_s.tokenize(__magic_name__   )
																			snake_case_						:   List[str]           =  tokenizer_r.tokenize(__magic_name__   )

																			self.assertListEqual(__magic_name__ ,							__magic_name__   )

															# Test that the tokenization is identical on unicode of line break type
															snake_case_						:   Optional[int]           =  [
															    '''\u000A''',  # (line feed, '\n')
															    '''\r\n''',  # (carriage return and line feed, '\r\n')
															    '''\u000D''',  # (carriage return, '\r')
															    '''\r''',  # (carriage return, '\r')
															    '''\u000D''',  # (carriage return, '\r')
															    '''\u2028''',  # (line separator)
															    '''\u2029''',  # (paragraph separator)
															    # "\u0085", # (next line)
															]

															# The tokenization is not identical for the character "\u0085" (next line). The slow version using ftfy transforms
															# it into the Horizontal Ellipsis character "…" ("\u2026") while the fast version transforms it into a
															# space (and thus into an empty list).

															for unicode_seq in line_break_unicodes:
																			snake_case_						:   int           =  tokenizer_s.tokenize(__magic_name__   )
																			snake_case_						:   Any           =  tokenizer_r.tokenize(__magic_name__   )

																			self.assertListEqual(__magic_name__ ,							__magic_name__   )






			def       lowerCamelCase  (self   )					->       Optional[Any]:


							'''simple docstring'''



							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
											with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''   ):
															snake_case_						:   int           =  '''hello'''  # `hello` is a token in the vocabulary of `pretrained_name`
															snake_case_						:   List[str]           =  F'''{text_of_1_token} {text_of_1_token}'''

															snake_case_						:   int           =  self.rust_tokenizer_class.from_pretrained(
															    __magic_name__ ,							use_fast=__magic_name__ ,							)
															snake_case_						:   Union[str, Any]           =  tokenizer_r(__magic_name__ ,							return_offsets_mapping=__magic_name__ ,							add_special_tokens=__magic_name__   )
															self.assertEqual(encoding.offset_mapping[0] ,							(0, len(__magic_name__   ))   )
															self.assertEqual(
															    encoding.offset_mapping[1] ,							(len(__magic_name__   ) + 1, len(__magic_name__   ) + 1 + len(__magic_name__   )) ,							)

															snake_case_						:   List[str]           =  F''' {text}'''

															snake_case_						:   List[str]           =  self.rust_tokenizer_class.from_pretrained(
															    __magic_name__ ,							use_fast=__magic_name__ ,							)
															snake_case_						:   int           =  tokenizer_r(__magic_name__ ,							return_offsets_mapping=__magic_name__ ,							add_special_tokens=__magic_name__   )
															self.assertEqual(encoding.offset_mapping[0] ,							(1, 1 + len(__magic_name__   ))   )
															self.assertEqual(
															    encoding.offset_mapping[1] ,							(1 + len(__magic_name__   ) + 1, 1 + len(__magic_name__   ) + 1 + len(__magic_name__   )) ,							)






			def       lowerCamelCase  (self   )					->       List[Any]:


							'''simple docstring'''



							with self.assertRaises(__magic_name__   ) as context:
											self.rust_tokenizer_class.from_pretrained('''robot-test/old-clip-tokenizer'''   )

							self.assertTrue(
							    context.exception.args[0].startswith(
							        '''The `backend_tokenizer` provided does not match the expected format.'''   )   )






			@require_ftfy
			def       lowerCamelCase  (self   )					->       int:


							'''simple docstring'''



							super().test_tokenization_python_rust_equals()






			def       lowerCamelCase  (self   )					->       List[Any]:


							'''simple docstring'''



							pass







import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()



class  __lowerCAmelCase	(      unittest.TestCase					):






			@property
			def       lowerCamelCase  (self   )					->       Union[str, Any]:


							'''simple docstring'''



							torch.manual_seed(0   )
							snake_case_						:   int           =  UNetaDModel(
							    block_out_channels=(32, 64) ,							layers_per_block=2 ,							sample_size=32 ,							in_channels=3 ,							out_channels=3 ,							down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''') ,							up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''') ,							)
							return model






			def       lowerCamelCase  (self   )					->       Dict:


							'''simple docstring'''



							snake_case_						:   Any           =  self.dummy_uncond_unet
							snake_case_						:   Optional[Any]           =  PNDMScheduler()

							snake_case_						:   Optional[Any]           =  PNDMPipeline(unet=__magic_name__ ,							scheduler=__magic_name__   )
							pndm.to(__magic_name__   )
							pndm.set_progress_bar_config(disable=__magic_name__   )

							snake_case_						:   str           =  torch.manual_seed(0   )
							snake_case_						:   Dict           =  pndm(generator=__magic_name__ ,							num_inference_steps=20 ,							output_type='''numpy'''   ).images

							snake_case_						:   str           =  torch.manual_seed(0   )
							snake_case_						:   str           =  pndm(generator=__magic_name__ ,							num_inference_steps=20 ,							output_type='''numpy''' ,							return_dict=__magic_name__   )[0]

							snake_case_						:   Any           =  image[0, -3:, -3:, -1]
							snake_case_						:   str           =  image_from_tuple[0, -3:, -3:, -1]

							assert image.shape == (1, 32, 32, 3)
							snake_case_						:   str           =  np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0]   )

							assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2
							assert np.abs(image_from_tuple_slice.flatten() - expected_slice   ).max() < 1e-2




@slow
@require_torch
class  __lowerCAmelCase	(      unittest.TestCase					):






			def       lowerCamelCase  (self   )					->       Any:


							'''simple docstring'''



							snake_case_						:   Tuple           =  '''google/ddpm-cifar10-32'''

							snake_case_						:   Tuple           =  UNetaDModel.from_pretrained(__magic_name__   )
							snake_case_						:   Optional[Any]           =  PNDMScheduler()

							snake_case_						:   Any           =  PNDMPipeline(unet=__magic_name__ ,							scheduler=__magic_name__   )
							pndm.to(__magic_name__   )
							pndm.set_progress_bar_config(disable=__magic_name__   )
							snake_case_						:   int           =  torch.manual_seed(0   )
							snake_case_						:   Tuple           =  pndm(generator=__magic_name__ ,							output_type='''numpy'''   ).images

							snake_case_						:   List[str]           =  image[0, -3:, -3:, -1]

							assert image.shape == (1, 32, 32, 3)
							snake_case_						:   str           =  np.array([0.1_564, 0.14_645, 0.1_406, 0.14_715, 0.12_425, 0.14_045, 0.13_115, 0.12_175, 0.125]   )

							assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2
