






"""simple docstring"""







import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import BatchEncoding, MarianTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import is_sentencepiece_available, is_tf_available, is_torch_available


if is_sentencepiece_available():
			from transformers.models.marian.tokenization_marian import VOCAB_FILES_NAMES, save_json

from ...test_tokenization_common import TokenizerTesterMixin


a					:							Optional[int]    =						get_tests_dir("""fixtures/test_sentencepiece.model""")

a					:							List[str]    =						{"""target_lang""": """fi""", """source_lang""": """en"""}
a					:							Union[str, Any]    =						""">>zh<<"""
a					:							Optional[Any]    =						"""Helsinki-NLP/"""

if is_torch_available():
			a					:							List[Any]    =						"""pt"""
elif is_tf_available():
			a					:							List[str]    =						"""tf"""
else:
			a					:							Tuple    =						"""jax"""


@require_sentencepiece
class        __UpperCAmelCase(  __A    ,    unittest.TestCase				):




						"""simple docstring"""




						__lowerCamelCase										=   MarianTokenizer
						__lowerCamelCase										=   False
						__lowerCamelCase										=   True




						def 						UpperCAmelCase_			(  self     ):
												'''simple docstring'''
												super().setUp()
												lowercase__							: List[Any]=			["</s>", "<unk>", "▁This", "▁is", "▁a", "▁t", "est", "\u0120", "<pad>"]
												lowercase__							: Tuple=			dict(zip(lowerCAmelCase_			,    range(len(lowerCAmelCase_     )     )     )     )
												lowercase__							: Optional[Any]=			Path(self.tmpdirname     )
												save_json(lowerCAmelCase_			,    save_dir / VOCAB_FILES_NAMES["vocab"]     )
												save_json(lowerCAmelCase_			,    save_dir / VOCAB_FILES_NAMES["tokenizer_config_file"]     )
												if not (save_dir / VOCAB_FILES_NAMES["source_spm"]).exists():
																		copyfile(lowerCAmelCase_			,    save_dir / VOCAB_FILES_NAMES["source_spm"]     )
																		copyfile(lowerCAmelCase_			,    save_dir / VOCAB_FILES_NAMES["target_spm"]     )

												lowercase__							: str=			MarianTokenizer.from_pretrained(self.tmpdirname     )
												tokenizer.save_pretrained(self.tmpdirname     )




						def 						UpperCAmelCase_			(  self			,    **snake_case__     ):
												'''simple docstring'''
												return MarianTokenizer.from_pretrained(self.tmpdirname			,    **lowerCAmelCase_     )




						def 						UpperCAmelCase_			(  self			,    snake_case__     ):
												'''simple docstring'''
												return (
												    "This is a test",
												    "This is a test",
												)




						def 						UpperCAmelCase_			(  self     ):
												'''simple docstring'''
												lowercase__							: List[str]=			"</s>"
												lowercase__							: Tuple=			0

												self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCAmelCase_     )			,    lowerCAmelCase_     )
												self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCAmelCase_     )			,    lowerCAmelCase_     )




						def 						UpperCAmelCase_			(  self     ):
												'''simple docstring'''
												lowercase__							: Union[str, Any]=			list(self.get_tokenizer().get_vocab().keys()     )

												self.assertEqual(vocab_keys[0]			,    "</s>"     )
												self.assertEqual(vocab_keys[1]			,    "<unk>"     )
												self.assertEqual(vocab_keys[-1]			,    "<pad>"     )
												self.assertEqual(len(lowerCAmelCase_     )			,    9     )




						def 						UpperCAmelCase_			(  self     ):
												'''simple docstring'''
												self.assertEqual(self.get_tokenizer().vocab_size			,    9     )




						def 						UpperCAmelCase_			(  self     ):
												'''simple docstring'''
												lowercase__							: List[str]=			MarianTokenizer.from_pretrained(F'''{ORG_NAME}opus-mt-en-de'''     )
												lowercase__							: Optional[int]=			en_de_tokenizer(["I am a small frog"]			,    return_tensors=lowerCAmelCase_     )
												self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_     )
												lowercase__							: Any=			[38, 121, 14, 697, 38848, 0]
												self.assertListEqual(lowerCAmelCase_			,    batch.input_ids[0]     )

												lowercase__							: Optional[Any]=			tempfile.mkdtemp()
												en_de_tokenizer.save_pretrained(lowerCAmelCase_     )
												lowercase__							: Tuple=			[x.name for x in Path(lowerCAmelCase_     ).glob("*"     )]
												self.assertIn("source.spm"			,    lowerCAmelCase_     )
												MarianTokenizer.from_pretrained(lowerCAmelCase_     )




						def 						UpperCAmelCase_			(  self     ):
												'''simple docstring'''
												lowercase__							: Optional[int]=			self.get_tokenizer()

												lowercase__							: Tuple=			tok(
												    ["I am a small frog" * 1000, "I am a small frog"]			,    padding=lowerCAmelCase_			,    truncation=lowerCAmelCase_			,    return_tensors=lowerCAmelCase_     )
												self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_     )
												self.assertEqual(batch.input_ids.shape			,    (2, 512)     )




						def 						UpperCAmelCase_			(  self     ):
												'''simple docstring'''
												lowercase__							: List[Any]=			self.get_tokenizer()
												lowercase__							: Any=			tok(["I am a tiny frog", "I am a small frog"]			,    padding=lowerCAmelCase_			,    return_tensors=lowerCAmelCase_     )
												self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_     )
												self.assertEqual(batch_smaller.input_ids.shape			,    (2, 10)     )




						@slow
						def 						UpperCAmelCase_			(  self     ):
												'''simple docstring'''
												# fmt: off
												lowercase__							: int=			{"input_ids": [[43495, 462, 20, 42164, 1369, 52, 464, 132, 1703, 492, 13, 7491, 38999, 6, 8, 464, 132, 1703, 492, 13, 4669, 37867, 13, 7525, 27, 1593, 988, 13, 33972, 7029, 6, 20, 8251, 383, 2, 270, 5866, 3788, 2, 2353, 8251, 12338, 2, 13958, 387, 2, 3629, 6953, 188, 2900, 2, 13958, 8011, 11501, 23, 8460, 4073, 34009, 20, 435, 11439, 27, 8, 8460, 4073, 6004, 20, 9988, 375, 27, 33, 266, 1945, 1076, 1350, 37867, 3288, 5, 577, 1076, 4374, 8, 5082, 5, 26453, 257, 556, 403, 2, 242, 132, 383, 316, 492, 8, 10767, 6, 316, 304, 4239, 3, 0], [148, 15722, 19, 1839, 12, 1350, 13, 22327, 5082, 5418, 47567, 35938, 59, 318, 19552, 108, 2183, 54, 14976, 4835, 32, 547, 1114, 8, 315, 2417, 5, 92, 19088, 3, 0, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100], [36, 6395, 12570, 39147, 11597, 6, 266, 4, 45405, 7296, 3, 0, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
												# fmt: on

												self.tokenizer_integration_test_util(
												    expected_encoding=lowerCAmelCase_			,    model_name="Helsinki-NLP/opus-mt-en-de"			,    revision="1a8c2263da11e68e50938f97e10cd57820bd504c"			,    decode_kwargs={"use_source_tokenizer": True}			,    )




						def 						UpperCAmelCase_			(  self     ):
												'''simple docstring'''
												lowercase__							: int=			MarianTokenizer.from_pretrained("hf-internal-testing/test-marian-two-vocabs"     )

												lowercase__							: Dict=			"Tämä on testi"
												lowercase__							: Optional[Any]=			"This is a test"

												lowercase__							: Union[str, Any]=			[76, 7, 2047, 2]
												lowercase__							: Optional[int]=			[69, 12, 11, 940, 2]

												lowercase__							: int=			tokenizer(lowerCAmelCase_     ).input_ids
												self.assertListEqual(lowerCAmelCase_			,    lowerCAmelCase_     )

												lowercase__							: Union[str, Any]=			tokenizer(text_target=lowerCAmelCase_     ).input_ids
												self.assertListEqual(lowerCAmelCase_			,    lowerCAmelCase_     )

												lowercase__							: List[Any]=			tokenizer.decode(lowerCAmelCase_			,    skip_special_tokens=lowerCAmelCase_     )
												self.assertEqual(lowerCAmelCase_			,    lowerCAmelCase_     )














"""simple docstring"""
from typing import Dict, List, Optional, Tuple, Union

import torch

from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class      UpperCamelCase_			(__A	):





	def __init__( self   :					List[Any]      ,      lowerCAmelCase_   :					TransformeraDModel      ,      lowerCAmelCase_   :					AutoencoderKL      ,      lowerCAmelCase_   :					KarrasDiffusionSchedulers      ,      lowerCAmelCase_   :					Optional[Dict[int, str]] = None      ,      )		->     List[Any]:
				super().__init__()
				self.register_modules(transformer=lowerCAmelCase_      ,      vae=lowerCAmelCase_      ,      scheduler=lowerCAmelCase_		)

				# create a imagenet -> id dictionary for easier use
				UpperCAmelCase_	:					str     	=   {}
				if idalabel is not None:
							for key, value in idalabel.items():
										for label in value.split(","		):
													UpperCAmelCase_	:					Union[str, Any]     	=   int(lowerCAmelCase_		)
							UpperCAmelCase_	:					Optional[int]     	=   dict(sorted(self.labels.items()		)		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					str      ,      lowerCAmelCase_   :					Union[str, List[str]]		)		->     List[int]:

				if not isinstance(lowerCAmelCase_      ,      lowerCAmelCase_		):
							UpperCAmelCase_	:					str     	=   list(lowerCAmelCase_		)

				for l in label:
							if l not in self.labels:
										raise ValueError(
										    f"""{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}."""		)

				return [self.labels[l] for l in label]





	@torch.no_grad()
	def __call__( self   :					List[str]      ,      lowerCAmelCase_   :					List[int]      ,      lowerCAmelCase_   :					float = 4.0      ,      lowerCAmelCase_   :					Optional[Union[torch.Generator, List[torch.Generator]]] = None      ,      lowerCAmelCase_   :					int = 50      ,      lowerCAmelCase_   :					Optional[str] = "pil"      ,      lowerCAmelCase_   :					bool = True      ,      )		->     Union[ImagePipelineOutput, Tuple]:
				UpperCAmelCase_	:					Optional[int]     	=   len(lowerCAmelCase_		)
				UpperCAmelCase_	:					int     	=   self.transformer.config.sample_size
				UpperCAmelCase_	:					Optional[Any]     	=   self.transformer.config.in_channels

				UpperCAmelCase_	:					Tuple     	=   randn_tensor(
				    shape=(batch_size, latent_channels, latent_size, latent_size)      ,      generator=lowerCAmelCase_      ,      device=self.device      ,      dtype=self.transformer.dtype      ,      )
				UpperCAmelCase_	:					str     	=   torch.cat([latents] * 2		) if guidance_scale > 1 else latents

				UpperCAmelCase_	:					List[Any]     	=   torch.tensor(lowerCAmelCase_      ,      device=self.device		).reshape(-1		)
				UpperCAmelCase_	:					Optional[Any]     	=   torch.tensor([1_000] * batch_size      ,      device=self.device		)
				UpperCAmelCase_	:					int     	=   torch.cat([class_labels, class_null]      ,      0		) if guidance_scale > 1 else class_labels

				# set step values
				self.scheduler.set_timesteps(lowerCAmelCase_		)

				for t in self.progress_bar(self.scheduler.timesteps		):
							if guidance_scale > 1:
										UpperCAmelCase_	:					Optional[Any]     	=   latent_model_input[: len(lowerCAmelCase_		) // 2]
										UpperCAmelCase_	:					int     	=   torch.cat([half, half]      ,      dim=0		)
							UpperCAmelCase_	:					Optional[int]     	=   self.scheduler.scale_model_input(lowerCAmelCase_      ,      lowerCAmelCase_		)

							UpperCAmelCase_	:					Union[str, Any]     	=   t
							if not torch.is_tensor(lowerCAmelCase_		):
										# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
										# This would be a good case for the `match` statement (Python 3.10+)
										UpperCAmelCase_	:					List[Any]     	=   latent_model_input.device.type == "mps"
										if isinstance(lowerCAmelCase_      ,      lowerCAmelCase_		):
													UpperCAmelCase_	:					Optional[int]     	=   torch.floataa if is_mps else torch.floataa
										else:
													UpperCAmelCase_	:					int     	=   torch.intaa if is_mps else torch.intaa
										UpperCAmelCase_	:					Optional[int]     	=   torch.tensor([timesteps]      ,      dtype=lowerCAmelCase_      ,      device=latent_model_input.device		)
							elif len(timesteps.shape		) == 0:
										UpperCAmelCase_	:					List[Any]     	=   timesteps[None].to(latent_model_input.device		)
							# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
							UpperCAmelCase_	:					List[Any]     	=   timesteps.expand(latent_model_input.shape[0]		)
							# predict noise model_output
							UpperCAmelCase_	:					List[Any]     	=   self.transformer(
							    lowerCAmelCase_      ,      timestep=lowerCAmelCase_      ,      class_labels=lowerCAmelCase_		).sample

							# perform guidance
							if guidance_scale > 1:
										UpperCAmelCase_	,      UpperCAmelCase_	:					Optional[int]     	=   noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
										UpperCAmelCase_	,      UpperCAmelCase_	:					Union[str, Any]     	=   torch.split(lowerCAmelCase_      ,      len(lowerCAmelCase_		) // 2      ,      dim=0		)

										UpperCAmelCase_	:					Optional[Any]     	=   uncond_eps + guidance_scale * (cond_eps - uncond_eps)
										UpperCAmelCase_	:					Tuple     	=   torch.cat([half_eps, half_eps]      ,      dim=0		)

										UpperCAmelCase_	:					Optional[Any]     	=   torch.cat([eps, rest]      ,      dim=1		)

							# learned sigma
							if self.transformer.config.out_channels // 2 == latent_channels:
										UpperCAmelCase_	,      UpperCAmelCase_	:					List[str]     	=   torch.split(lowerCAmelCase_      ,      lowerCAmelCase_      ,      dim=1		)
							else:
										UpperCAmelCase_	:					Optional[Any]     	=   noise_pred

							# compute previous image: x_t -> x_t-1
							UpperCAmelCase_	:					Union[str, Any]     	=   self.scheduler.step(lowerCAmelCase_      ,      lowerCAmelCase_      ,      lowerCAmelCase_		).prev_sample

				if guidance_scale > 1:
							UpperCAmelCase_	,      UpperCAmelCase_	:					Optional[Any]     	=   latent_model_input.chunk(2      ,      dim=0		)
				else:
							UpperCAmelCase_	:					Optional[int]     	=   latent_model_input

				UpperCAmelCase_	:					Dict     	=   1 / self.vae.config.scaling_factor * latents
				UpperCAmelCase_	:					Dict     	=   self.vae.decode(lowerCAmelCase_		).sample

				UpperCAmelCase_	:					Tuple     	=   (samples / 2 + 0.5).clamp(0      ,      1		)

				# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
				UpperCAmelCase_	:					Optional[Any]     	=   samples.cpu().permute(0      ,      2      ,      3      ,      1		).float().numpy()

				if output_type == "pil":
							UpperCAmelCase_	:					Optional[int]     	=   self.numpy_to_pil(lowerCAmelCase_		)

				if not return_dict:
							return (samples,)

				return ImagePipelineOutput(images=lowerCAmelCase_		)



