


'''simple docstring'''

import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import require_keras_nlp, require_tf, slow


if is_tf_available():
	import tensorflow as tf

if is_keras_nlp_available():
	from transformers.models.gpta import TFGPTaTokenizer


_UpperCAmelCase				: str							    =				["""gpt2"""]
_UpperCAmelCase				: Optional[int]							    =				"""gpt2"""

if is_tf_available():


	class       a__				( tf.Module      ):







					"""simple docstring"""



					def __init__(self       ,			__lowercase		):
											super().__init__()
											__lowerCAmelCase							       =				tokenizer
											__lowerCAmelCase							       =				AutoConfig.from_pretrained(__lowercase		)
											__lowerCAmelCase							       =				TFGPTaLMHeadModel.from_config(__lowercase		)




					@tf.function(input_signature=(tf.TensorSpec((None,)       ,			tf.string       ,			name='''text'''		),)		)
					def 					_snake_case  (self       ,			__lowercase		):
											__lowerCAmelCase							       =				self.tokenizer(__lowercase		)
											__lowerCAmelCase							       =				tokenized['''input_ids'''].to_tensor()

											__lowerCAmelCase							       =				tf.cast(input_ids_dense > 0       ,			tf.intaa		)
											# input_mask = tf.reshape(input_mask, [-1, MAX_SEQ_LEN])

											__lowerCAmelCase							       =				self.model(input_ids=__lowercase       ,			attention_mask=__lowercase		)['''logits''']

											return outputs


@require_tf
@require_keras_nlp
class       a__				( unittest.TestCase      ):







				"""simple docstring"""



				def 					_snake_case  (self		):
										super().setUp()

										__lowerCAmelCase							       =				[GPTaTokenizer.from_pretrained(__lowercase		) for checkpoint in (TOKENIZER_CHECKPOINTS)]
										__lowerCAmelCase							       =				[TFGPTaTokenizer.from_pretrained(__lowercase		) for checkpoint in TOKENIZER_CHECKPOINTS]
										assert len(self.tokenizers		) == len(self.tf_tokenizers		)

										__lowerCAmelCase							       =				[
										    '''This is a straightforward English test sentence.''',
										    '''This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.''',
										    '''Now we\'re going to add some Chinese: 一 二 三 一二三''',
										    '''And some much more rare Chinese: 齉 堃 齉堃''',
										    '''Je vais aussi écrire en français pour tester les accents''',
										    '''Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ''',
										]
										__lowerCAmelCase							       =				list(zip(self.test_sentences       ,			self.test_sentences[::-1]		)		)




				def 					_snake_case  (self		):
										for tokenizer, tf_tokenizer in zip(self.tokenizers       ,			self.tf_tokenizers		):
																for test_inputs in self.test_sentences:
																						__lowerCAmelCase							       =				tokenizer([test_inputs]       ,			return_tensors='''tf'''		)
																						__lowerCAmelCase							       =				tf_tokenizer([test_inputs]		)

																						for key in python_outputs.keys():
																												# convert them to numpy to avoid messing with ragged tensors
																												__lowerCAmelCase							       =				python_outputs[key].numpy()
																												__lowerCAmelCase							       =				tf_outputs[key].numpy()

																												self.assertTrue(tf.reduce_all(python_outputs_values.shape == tf_outputs_values.shape		)		)
																												self.assertTrue(tf.reduce_all(tf.cast(__lowercase       ,			tf.intaa		) == tf_outputs_values		)		)




				@slow
				def 					_snake_case  (self		):
										for tf_tokenizer in self.tf_tokenizers:
																__lowerCAmelCase							       =				tf.function(__lowercase		)
																for test_inputs in self.test_sentences:
																						__lowerCAmelCase							       =				tf.constant(__lowercase		)
																						__lowerCAmelCase							       =				compiled_tokenizer(__lowercase		)
																						__lowerCAmelCase							       =				tf_tokenizer(__lowercase		)

																						for key in eager_outputs.keys():
																												self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]		)		)




				@slow
				def 					_snake_case  (self		):
										for tf_tokenizer in self.tf_tokenizers:
																__lowerCAmelCase							       =				ModelToSave(tokenizer=__lowercase		)
																__lowerCAmelCase							       =				tf.convert_to_tensor([self.test_sentences[0]]		)
																__lowerCAmelCase							       =				model.serving(__lowercase		)  # Build model with some sample inputs
																with TemporaryDirectory() as tempdir:
																						__lowerCAmelCase							       =				Path(__lowercase		) / '''saved.model'''
																						tf.saved_model.save(__lowercase       ,			__lowercase       ,			signatures={'''serving_default''': model.serving}		)
																						__lowerCAmelCase							       =				tf.saved_model.load(__lowercase		)
																__lowerCAmelCase							       =				loaded_model.signatures['''serving_default'''](__lowercase		)['''output_0''']
																# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
																self.assertTrue(tf.reduce_all(out == loaded_output		)		)




				@slow
				def 					_snake_case  (self		):
										for tf_tokenizer in self.tf_tokenizers:
																__lowerCAmelCase							       =				tf.convert_to_tensor([self.test_sentences[0]]		)
																__lowerCAmelCase							       =				tf_tokenizer(__lowercase		)  # Build model with some sample inputs

																__lowerCAmelCase							       =				tf_tokenizer.get_config()
																__lowerCAmelCase							       =				TFGPTaTokenizer.from_config(__lowercase		)
																__lowerCAmelCase							       =				model_from_config(__lowercase		)

																for key in from_config_output.keys():
																						self.assertTrue(tf.reduce_all(from_config_output[key] == out[key]		)		)




				@slow
				def 					_snake_case  (self		):
										for tf_tokenizer in self.tf_tokenizers:
																# for the test to run
																__lowerCAmelCase							       =				12_31_23

																for max_length in [3, 5, 10_24]:
																						__lowerCAmelCase							       =				tf.convert_to_tensor([self.test_sentences[0]]		)
																						__lowerCAmelCase							       =				tf_tokenizer(__lowercase       ,			max_length=__lowercase		)

																						__lowerCAmelCase							       =				out['''input_ids'''].numpy().shape[1]

																						assert out_length == max_length





'''simple docstring'''

from typing import List, Optional, Tuple, Union

import PIL
import torch
from torchvision import transforms

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor


_UpperCAmelCase				: Tuple							    =				transforms.Compose(
    [
        transforms.Resize((2_5_6, 2_5_6)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)

def 							__magic_name__(						lowerCamelCase):
						if isinstance(lowerCamelCase,				torch.Tensor):
												return image
						elif isinstance(lowerCamelCase,				PIL.Image.Image):
												__lowerCAmelCase							       =				[image]

						__lowerCAmelCase							       =				[trans(img.convert('''RGB''')) for img in image]
						__lowerCAmelCase							       =				torch.stack(lowerCamelCase)
						return image




class       a__				( __A      ):







				"""simple docstring"""



				def __init__(self       ,			__lowercase       ,			__lowercase		):
										super().__init__()

										# make sure scheduler can always be converted to DDIM
										__lowerCAmelCase							       =				DDIMScheduler.from_config(scheduler.config		)

										self.register_modules(unet=__lowercase       ,			scheduler=__lowercase		)




				def 					_snake_case  (self       ,			__lowercase		):
										if strength < 0 or strength > 1:
																raise ValueError(F"""The value of strength should in [0.0, 1.0] but is {strength}"""		)




				def 					_snake_case  (self       ,			__lowercase       ,			__lowercase       ,			__lowercase		):
										# get the original timestep using init_timestep
										__lowerCAmelCase							       =				min(int(num_inference_steps * strength		)       ,			__lowercase		)

										__lowerCAmelCase							       =				max(num_inference_steps - init_timestep       ,			0		)
										__lowerCAmelCase							       =				self.scheduler.timesteps[t_start:]

										return timesteps, num_inference_steps - t_start




				def 					_snake_case  (self       ,			__lowercase       ,			__lowercase       ,			__lowercase       ,			__lowercase       ,			__lowercase       ,			__lowercase=None		):
										if not isinstance(__lowercase       ,			(torch.Tensor, PIL.Image.Image, list)		):
																raise ValueError(
																    F"""`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(__lowercase		)}"""		)

										__lowerCAmelCase							       =				image.to(device=__lowercase       ,			dtype=__lowercase		)

										if isinstance(__lowercase       ,			__lowercase		) and len(__lowercase		) != batch_size:
																raise ValueError(
																    F"""You have passed a list of generators of length {len(__lowercase		)}, but requested an effective batch"""
																    F""" size of {batch_size}. Make sure the batch size matches the length of the generators."""		)

										__lowerCAmelCase							       =				init_latents.shape
										__lowerCAmelCase							       =				randn_tensor(__lowercase       ,			generator=__lowercase       ,			device=__lowercase       ,			dtype=__lowercase		)

										# get latents
										print('''add noise to latents at timestep'''       ,			__lowercase		)
										__lowerCAmelCase							       =				self.scheduler.add_noise(__lowercase       ,			__lowercase       ,			__lowercase		)
										__lowerCAmelCase							       =				init_latents

										return latents




				@torch.no_grad()
				def __call__(self       ,			__lowercase = None       ,			__lowercase = 0.8       ,			__lowercase = 1       ,			__lowercase = None       ,			__lowercase = 0.0       ,			__lowercase = 50       ,			__lowercase = None       ,			__lowercase = "pil"       ,			__lowercase = True       ,			):
										self.check_inputs(__lowercase		)

										# 2. Preprocess image
										__lowerCAmelCase							       =				preprocess(__lowercase		)

										# 3. set timesteps
										self.scheduler.set_timesteps(__lowercase       ,			device=self.device		)
										__lowerCAmelCase       ,					__lowerCAmelCase							       =				self.get_timesteps(__lowercase       ,			__lowercase       ,			self.device		)
										__lowerCAmelCase							       =				timesteps[:1].repeat(__lowercase		)

										# 4. Prepare latent variables
										__lowerCAmelCase							       =				self.prepare_latents(__lowercase       ,			__lowercase       ,			__lowercase       ,			self.unet.dtype       ,			self.device       ,			__lowercase		)
										__lowerCAmelCase							       =				latents

										# 5. Denoising loop
										for t in self.progress_bar(__lowercase		):
																# 1. predict noise model_output
																__lowerCAmelCase							       =				self.unet(__lowercase       ,			__lowercase		).sample

																# 2. predict previous mean of image x_t-1 and add variance depending on eta
																# eta corresponds to η in paper and should be between [0, 1]
																# do x_t -> x_t-1
																__lowerCAmelCase							       =				self.scheduler.step(
																    __lowercase       ,			__lowercase       ,			__lowercase       ,			eta=__lowercase       ,			use_clipped_model_output=__lowercase       ,			generator=__lowercase       ,			).prev_sample

										__lowerCAmelCase							       =				(image / 2 + 0.5).clamp(0       ,			1		)
										__lowerCAmelCase							       =				image.cpu().permute(0       ,			2       ,			3       ,			1		).numpy()
										if output_type == "pil":
																__lowerCAmelCase							       =				self.numpy_to_pil(__lowercase		)

										if not return_dict:
																return (image, latent_timestep.item())

										return ImagePipelineOutput(images=__lowercase		)

