
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 				UpperCAmelCase						( _UpperCamelCase    ,			unittest.TestCase       ):
					a__:			List[str]      	=					CLIPTokenizer
					a__:			Union[str, Any]      	=					CLIPTokenizerFast
					a__:			Tuple      	=					True
					a__:			Optional[Any]      	=					{}
					a__:			int      	=					False

					def 							_lowerCAmelCase						(      self   :       List[Any]       ):
											super().setUp()

											# fmt: off
											lowercase							:     List[str]									=	['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
											lowercase							:     List[str]									=	dict(zip(_UpperCAmelCase			,   range(len(_UpperCAmelCase       )       )       )       )
											lowercase							:     int									=	['#version: 0.2', 'l o', 'lo w</w>', 'e r</w>']
											lowercase							:     str									=	{'unk_token': '<unk>'}

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

					def 							_lowerCAmelCase						(      self   :       Optional[int]			,   **lowerCAmelCase   :       List[Any]       ):
											kwargs.update(self.special_tokens_map       )
											return CLIPTokenizer.from_pretrained(self.tmpdirname			,   **_UpperCAmelCase       )

					def 							_lowerCAmelCase						(      self   :       List[Any]			,   **lowerCAmelCase   :       str       ):
											kwargs.update(self.special_tokens_map       )
											return CLIPTokenizerFast.from_pretrained(self.tmpdirname			,   **_UpperCAmelCase       )

					def 							_lowerCAmelCase						(      self   :       Tuple			,   lowerCAmelCase   :       Tuple       ):
											lowercase							:     Any									=	'lower newer'
											lowercase							:     List[str]									=	'lower newer'
											return input_text, output_text

					def 							_lowerCAmelCase						(      self   :       Tuple       ):
											lowercase							:     int									=	CLIPTokenizer(self.vocab_file			,   self.merges_file			,   **self.special_tokens_map       )
											lowercase							:     Any									=	'lower newer'
											lowercase							:     int									=	['lo', 'w', 'er</w>', 'n', 'e', 'w', 'er</w>']
											lowercase							:     Optional[Any]									=	tokenizer.tokenize(_UpperCAmelCase       )
											self.assertListEqual(_UpperCAmelCase			,   _UpperCAmelCase       )

											lowercase							:     Union[str, Any]									=	tokens + [tokenizer.unk_token]
											lowercase							:     str									=	[10, 2, 16, 9, 3, 2, 16, 20]
											self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCAmelCase       )			,   _UpperCAmelCase       )

					@require_ftfy
					def 							_lowerCAmelCase						(      self   :       Optional[Any]       ):
											for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																	with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''       ):
																							lowercase							:     Optional[int]									=	self.tokenizer_class.from_pretrained(_UpperCAmelCase			,   **_UpperCAmelCase       )
																							lowercase							:     List[str]									=	self.rust_tokenizer_class.from_pretrained(_UpperCAmelCase			,   **_UpperCAmelCase       )

																							lowercase							:     int									=	'A\n\'ll 11p223RF☆ho!!to?\'d\'d\'\'d of a cat to-$\'\'d.'
																							lowercase							:     Optional[Any]									=	tokenizer_s.tokenize(_UpperCAmelCase       )
																							lowercase							:     List[Any]									=	tokenizer_r.tokenize(_UpperCAmelCase       )

																							self.assertListEqual(_UpperCAmelCase			,   _UpperCAmelCase       )

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

																							self.assertListEqual(_UpperCAmelCase			,   _UpperCAmelCase       )

																							# Test that the tokenization is identical on unicode of space type
																							lowercase							:     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:
																													lowercase							:     str									=	tokenizer_s.tokenize(_UpperCAmelCase       )
																													lowercase							:     List[Any]									=	tokenizer_r.tokenize(_UpperCAmelCase       )

																													self.assertListEqual(_UpperCAmelCase			,   _UpperCAmelCase       )

																							# Test that the tokenization is identical on unicode of line break type
																							lowercase							:     List[Any]									=	[
																							    '\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:
																													lowercase							:     str									=	tokenizer_s.tokenize(_UpperCAmelCase       )
																													lowercase							:     Tuple									=	tokenizer_r.tokenize(_UpperCAmelCase       )

																													self.assertListEqual(_UpperCAmelCase			,   _UpperCAmelCase       )

					def 							_lowerCAmelCase						(      self   :       Union[str, Any]       ):
											# Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space`
											for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																	with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''       ):
																							lowercase							:     Union[str, Any]									=	'hello'  # `hello` is a token in the vocabulary of `pretrained_name`
																							lowercase							:     Dict									=	f'''{text_of_1_token} {text_of_1_token}'''

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

																							lowercase							:     List[str]									=	f''' {text}'''

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

					def 							_lowerCAmelCase						(      self   :       Dict       ):
											# Test related to the breaking change introduced in transformers v4.17.0
											# We need to check that an error in raised when the user try to load a previous version of the tokenizer.
											with self.assertRaises(_UpperCAmelCase       ) 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   :       List[Any]       ):
											super().test_tokenization_python_rust_equals()




					def 							_lowerCAmelCase						(      self   :       str       ):
											# CLIP always lower cases letters
											pass







'''simple docstring'''

import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel

from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()

class       __magic_name__		(       unittest.TestCase  ):







							@property
							def 				__lowercase     (				self   :			int						):
									torch.manual_seed(0						)
									_a	: 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







							@property
							def 				__lowercase     (				self   :			Any						):
									torch.manual_seed(0						)
									_a	: int								=				VQModel(
									    block_out_channels=[32, 64]      ,in_channels=3      ,out_channels=3      ,down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']      ,up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']      ,latent_channels=3      ,)
									return model







							@property
							def 				__lowercase     (				self   :			Union[str, Any]						):
									torch.manual_seed(0						)
									_a	: Dict								=				CLIPTextConfig(
									    bos_token_id=0      ,eos_token_id=2      ,hidden_size=32      ,intermediate_size=37      ,layer_norm_eps=1E-05      ,num_attention_heads=4      ,num_hidden_layers=5      ,pad_token_id=1      ,vocab_size=1000      ,)
									return CLIPTextModel(_UpperCAmelCase						)







							def 				__lowercase     (				self   :			Dict						):
									_a	: Union[str, Any]								=				self.dummy_uncond_unet
									_a	: Union[str, Any]								=				DDIMScheduler()
									_a	: Union[str, Any]								=				self.dummy_vq_model

									_a	: List[Any]								=				LDMPipeline(unet=_UpperCAmelCase      ,vqvae=_UpperCAmelCase      ,scheduler=_UpperCAmelCase						)
									ldm.to(_UpperCAmelCase						)
									ldm.set_progress_bar_config(disable=_UpperCAmelCase						)

									_a	: Dict								=				torch.manual_seed(0						)
									_a	: int								=				ldm(generator=_UpperCAmelCase      ,num_inference_steps=2      ,output_type='numpy'						).images

									_a	: Tuple								=				torch.manual_seed(0						)
									_a	: List[Any]								=				ldm(generator=_UpperCAmelCase      ,num_inference_steps=2      ,output_type='numpy'      ,return_dict=_UpperCAmelCase						)[0]

									_a	: Any								=				image[0, -3:, -3:, -1]
									_a	: List[Any]								=				image_from_tuple[0, -3:, -3:, -1]

									assert image.shape == (1, 64, 64, 3)
									_a	: Union[str, Any]								=				np.array([0.85_12, 0.8_18, 0.64_11, 0.68_08, 0.44_65, 0.56_18, 0.46, 0.62_31, 0.51_72]						)
									_a	: Any								=				1E-2 if torch_device != 'mps' else 3E-2

									assert np.abs(image_slice.flatten() - expected_slice						).max() < tolerance
									assert np.abs(image_from_tuple_slice.flatten() - expected_slice						).max() < tolerance

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







							def 				__lowercase     (				self   :			Optional[int]						):
									_a	: Dict								=				LDMPipeline.from_pretrained('CompVis/ldm-celebahq-256'						)
									ldm.to(_UpperCAmelCase						)
									ldm.set_progress_bar_config(disable=_UpperCAmelCase						)

									_a	: str								=				torch.manual_seed(0						)
									_a	: Optional[Any]								=				ldm(generator=_UpperCAmelCase      ,num_inference_steps=5      ,output_type='numpy'						).images

									_a	: Optional[Any]								=				image[0, -3:, -3:, -1]

									assert image.shape == (1, 256, 256, 3)
									_a	: Union[str, Any]								=				np.array([0.43_99, 0.4_49_75, 0.4_68_25, 0.4_74, 0.43_59, 0.45_81, 0.4_50_95, 0.43_41, 0.44_47]						)
									_a	: List[str]								=				1E-2 if torch_device != 'mps' else 3E-2

									assert np.abs(image_slice.flatten() - expected_slice						).max() < tolerance




