

"""simple docstring"""




import random
import unittest

import torch

from diffusers import IFSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin





@skip_mps
class      __UpperCamelCase					(  _A	,						_A	,						unittest.TestCase		):
		SCREAMING_SNAKE_CASE														=       IFSuperResolutionPipeline
		SCREAMING_SNAKE_CASE														=       TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"width", "height"}
		SCREAMING_SNAKE_CASE														=       TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
		SCREAMING_SNAKE_CASE														=       PipelineTesterMixin.required_optional_params - {"latents"}


		def    SCREAMING_SNAKE_CASE__  (self					:						str):
							return self._get_superresolution_dummy_components()


		def    SCREAMING_SNAKE_CASE__  (self					:						Dict	,	__SCREAMING_SNAKE_CASE					:						List[str]	,	__SCREAMING_SNAKE_CASE					:						Optional[Any]=0):
							if str(__SCREAMING_SNAKE_CASE).startswith("mps"):
												A     =   torch.manual_seed(__SCREAMING_SNAKE_CASE)
							else:
												A     =   torch.Generator(device=__SCREAMING_SNAKE_CASE).manual_seed(__SCREAMING_SNAKE_CASE)

							A     =   floats_tensor((1, 3, 3_2, 3_2)	,	rng=random.Random(__SCREAMING_SNAKE_CASE)).to(__SCREAMING_SNAKE_CASE)

							A     =   {
							    "prompt": "A painting of a squirrel eating a burger",
							    "image": image,
							    "generator": generator,
							    "num_inference_steps": 2,
							    "output_type": "numpy",
							}

							return inputs


		@unittest.skipIf(
		    torch_device != "cuda" or not is_xformers_available()	,	reason="XFormers attention is only available with CUDA and `xformers` installed"	,	)
		def    SCREAMING_SNAKE_CASE__  (self					:						List[str]):
							self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3)


		def    SCREAMING_SNAKE_CASE__  (self					:						Union[str, Any]):
							self._test_save_load_optional_components()


		@unittest.skipIf(torch_device != "cuda"	,	reason="float16 requires CUDA")
		def    SCREAMING_SNAKE_CASE__  (self					:						Union[str, Any]):
							# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
							super().test_save_load_floataa(expected_max_diff=1E-1)


		def    SCREAMING_SNAKE_CASE__  (self					:						Union[str, Any]):
							self._test_attention_slicing_forward_pass(expected_max_diff=1E-2)


		def    SCREAMING_SNAKE_CASE__  (self					:						Union[str, Any]):
							self._test_save_load_local()


		def    SCREAMING_SNAKE_CASE__  (self					:						Optional[Any]):
							self._test_inference_batch_single_identical(
							    expected_max_diff=1E-2	,	)








"""simple docstring"""




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      __UpperCamelCase					(  unittest.TestCase		):
		SCREAMING_SNAKE_CASE														=       EsmTokenizer


		def    SCREAMING_SNAKE_CASE__  (self					:						Tuple):
							super().setUp()
							A     =   tempfile.mkdtemp()
							# fmt: off
							A     =   ["<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     =   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    SCREAMING_SNAKE_CASE__  (self					:						Any	,	**__SCREAMING_SNAKE_CASE					:						str):
							return [self.get_tokenizer(**__SCREAMING_SNAKE_CASE)]


		def    SCREAMING_SNAKE_CASE__  (self					:						Union[str, Any]	,	**__SCREAMING_SNAKE_CASE					:						Optional[Any]):
							return self.tokenizer_class.from_pretrained(self.tmpdirname	,	**__SCREAMING_SNAKE_CASE)


		def    SCREAMING_SNAKE_CASE__  (self					:						List[Any]):
							A     =   self.tokenizer_class(self.vocab_file)

							A     =   tokenizer.tokenize("LAGVS")
							self.assertListEqual(__SCREAMING_SNAKE_CASE	,	["L", "A", "G", "V", "S"])
							self.assertListEqual(tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE)	,	[4, 5, 6, 7, 8])


		def    SCREAMING_SNAKE_CASE__  (self					:						List[Any]):
							A     =   self.tokenizer_class(self.vocab_file)

							A     =   "LAGVS"
							self.assertListEqual(tokenizer.encode(__SCREAMING_SNAKE_CASE)	,	[0, 4, 5, 6, 7, 8, 2])


		def    SCREAMING_SNAKE_CASE__  (self					:						List[str]):
							A     =   self.tokenizer_class(self.vocab_file)

							A     =   ["LAGVS", "WCB"]
							A     =   tokenizer(__SCREAMING_SNAKE_CASE	,	padding=__SCREAMING_SNAKE_CASE)["input_ids"]

							self.assertListEqual(__SCREAMING_SNAKE_CASE	,	[[0, 4, 5, 6, 7, 8, 2], [0, 2_2, 2_3, 2_5, 2]])


		def    SCREAMING_SNAKE_CASE__  (self					:						str):
							A     =   self.tokenizer_class(self.vocab_file)

							A     =   ["LAGVS", "WCB"]
							A     =   tokenizer(__SCREAMING_SNAKE_CASE	,	padding=__SCREAMING_SNAKE_CASE)["input_ids"]

							self.assertListEqual(__SCREAMING_SNAKE_CASE	,	[[0, 4, 5, 6, 7, 8, 2], [0, 2_2, 2_3, 2_5, 2, 1, 1]])


		def    SCREAMING_SNAKE_CASE__  (self					:						Tuple):
							A     =   self.get_tokenizers(fast=__SCREAMING_SNAKE_CASE)
							for tokenizer in tokenizers:
												with self.subTest(F"""{tokenizer.__class__.__name__}"""):
																	A     =   "<unk>"
																	A     =   "<mask>"

																	A     =   tokenizer.tokenize(__SCREAMING_SNAKE_CASE)
																	A     =   tokenizer.tokenize(__SCREAMING_SNAKE_CASE)

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





