





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       __A					(					lowercase_				):

			'''simple docstring'''







			def __init__(					self ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase = None ,						):



				'''simple docstring'''







				super().__init__()
				self.register_modules(transformer=__lowerCAmelCase ,						vae=__lowerCAmelCase ,						scheduler=__lowerCAmelCase							)

				# create a imagenet -> id dictionary for easier use
				lowerCamelCase__              =	{}
				if idalabel is not None:
					for key, value in idalabel.items():
						for label in value.split(''','''							):
							lowerCamelCase__              =	int(__lowerCAmelCase							)
					lowerCamelCase__              =	dict(sorted(self.labels.items()							)							)





			def 	__lowerCamelCase       (					self ,						__lowerCAmelCase							):



				'''simple docstring'''







				if not isinstance(__lowerCAmelCase ,						__lowerCAmelCase							):
					lowerCamelCase__              =	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 ,						__lowerCAmelCase ,						__lowerCAmelCase = 4.0 ,						__lowerCAmelCase = None ,						__lowerCAmelCase = 5_0 ,						__lowerCAmelCase = "pil" ,						__lowerCAmelCase = True ,						):



				'''simple docstring'''







				lowerCamelCase__              =	len(__lowerCAmelCase							)
				lowerCamelCase__              =	self.transformer.config.sample_size
				lowerCamelCase__              =	self.transformer.config.in_channels

				lowerCamelCase__              =	randn_tensor(
				    shape=(batch_size, latent_channels, latent_size, latent_size) ,						generator=__lowerCAmelCase ,						device=self.device ,						dtype=self.transformer.dtype ,						)
				lowerCamelCase__              =	torch.cat([latents] * 2							) if guidance_scale > 1 else latents

				lowerCamelCase__              =	torch.tensor(__lowerCAmelCase ,						device=self.device							).reshape(-1							)
				lowerCamelCase__              =	torch.tensor([1_0_0_0] * batch_size ,						device=self.device							)
				lowerCamelCase__              =	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:
						lowerCamelCase__              =	latent_model_input[: len(__lowerCAmelCase							) // 2]
						lowerCamelCase__              =	torch.cat([half, half] ,						dim=0							)
					lowerCamelCase__              =	self.scheduler.scale_model_input(__lowerCAmelCase ,						__lowerCAmelCase							)

					lowerCamelCase__              =	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+)
						lowerCamelCase__              =	latent_model_input.device.type == '''mps'''
						if isinstance(__lowerCAmelCase ,						__lowerCAmelCase							):
							lowerCamelCase__              =	torch.floataa if is_mps else torch.floataa
						else:
							lowerCamelCase__              =	torch.intaa if is_mps else torch.intaa
						lowerCamelCase__              =	torch.tensor([timesteps] ,						dtype=__lowerCAmelCase ,						device=latent_model_input.device							)
					elif len(timesteps.shape							) == 0:
						lowerCamelCase__              =	timesteps[None].to(latent_model_input.device							)
					# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
					lowerCamelCase__              =	timesteps.expand(latent_model_input.shape[0]							)
					# predict noise model_output
					lowerCamelCase__              =	self.transformer(
					    __lowerCAmelCase ,						timestep=__lowerCAmelCase ,						class_labels=__lowerCAmelCase							).sample

					# perform guidance
					if guidance_scale > 1:
						lowerCamelCase__       ,			lowerCamelCase__              =	noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
						lowerCamelCase__       ,			lowerCamelCase__              =	torch.split(__lowerCAmelCase ,						len(__lowerCAmelCase							) // 2 ,						dim=0							)

						lowerCamelCase__              =	uncond_eps + guidance_scale * (cond_eps - uncond_eps)
						lowerCamelCase__              =	torch.cat([half_eps, half_eps] ,						dim=0							)

						lowerCamelCase__              =	torch.cat([eps, rest] ,						dim=1							)

					# learned sigma
					if self.transformer.config.out_channels // 2 == latent_channels:
						lowerCamelCase__       ,			lowerCamelCase__              =	torch.split(__lowerCAmelCase ,						__lowerCAmelCase ,						dim=1							)
					else:
						lowerCamelCase__              =	noise_pred

					# compute previous image: x_t -> x_t-1
					lowerCamelCase__              =	self.scheduler.step(__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase							).prev_sample

				if guidance_scale > 1:
					lowerCamelCase__       ,			lowerCamelCase__              =	latent_model_input.chunk(2 ,						dim=0							)
				else:
					lowerCamelCase__              =	latent_model_input

				lowerCamelCase__              =	1 / self.vae.config.scaling_factor * latents
				lowerCamelCase__              =	self.vae.decode(__lowerCAmelCase							).sample

				lowerCamelCase__              =	(samples / 2 + 0.5).clamp(0 ,						1							)

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

				if output_type == "pil":
					lowerCamelCase__              =	self.numpy_to_pil(__lowerCAmelCase							)

				if not return_dict:
					return (samples,)

				return ImagePipelineOutput(images=__lowerCAmelCase							)








# Usage:
# ./gen-card-allenai-wmt16.py

import os
from pathlib import Path





def lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case ,__snake_case							)       ->   Any:







	'''simple docstring'''







	lowerCamelCase__              =	{
	    '''en''': '''Machine learning is great, isn\'t it?''',
	    '''ru''': '''Машинное обучение - это здорово, не так ли?''',
	    '''de''': '''Maschinelles Lernen ist großartig, nicht wahr?''',
	}

	# BLUE scores as follows:
	# "pair": [fairseq, transformers]
	lowerCamelCase__              =	{
	    '''wmt16-en-de-dist-12-1''': [2_8.3, 2_7.5_2],
	    '''wmt16-en-de-dist-6-1''': [2_7.4, 2_7.1_1],
	    '''wmt16-en-de-12-1''': [2_6.9, 2_5.7_5],
	}
	lowerCamelCase__              =	F'{src_lang}-{tgt_lang}'

	lowerCamelCase__              =	F'\n---\nlanguage:\n- {src_lang}\n- {tgt_lang}\nthumbnail:\ntags:\n- translation\n- wmt16\n- allenai\nlicense: apache-2.0\ndatasets:\n- wmt16\nmetrics:\n- bleu\n---\n\n# FSMT\n\n## Model description\n\nThis is a ported version of fairseq-based [wmt16 transformer](https://github.com/jungokasai/deep-shallow/) for {src_lang}-{tgt_lang}.\n\nFor more details, please, see [Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation](https://arxiv.org/abs/2006.10369).\n\nAll 3 models are available:\n\n* [wmt16-en-de-dist-12-1](https://huggingface.co/allenai/wmt16-en-de-dist-12-1)\n* [wmt16-en-de-dist-6-1](https://huggingface.co/allenai/wmt16-en-de-dist-6-1)\n* [wmt16-en-de-12-1](https://huggingface.co/allenai/wmt16-en-de-12-1)\n\n\n## Intended uses & limitations\n\n#### How to use\n\n```python\nfrom transformers import FSMTForConditionalGeneration, FSMTTokenizer\nmname = "allenai/{model_name}"\ntokenizer = FSMTTokenizer.from_pretrained(mname)\nmodel = FSMTForConditionalGeneration.from_pretrained(mname)\n\ninput = "{texts[src_lang]}"\ninput_ids = tokenizer.encode(input, return_tensors="pt")\noutputs = model.generate(input_ids)\ndecoded = tokenizer.decode(outputs[0], skip_special_tokens=True)\nprint(decoded) # {texts[tgt_lang]}\n\n```\n\n#### Limitations and bias\n\n\n## Training data\n\nPretrained weights were left identical to the original model released by allenai. For more details, please, see the [paper](https://arxiv.org/abs/2006.10369).\n\n## Eval results\n\nHere are the BLEU scores:\n\nmodel   | fairseq | transformers\n-------|---------|----------\n{model_name}  | {scores[model_name][0]} | {scores[model_name][1]}\n\nThe score is slightly below the score reported in the paper, as the researchers don\'t use `sacrebleu` and measure the score on tokenized outputs. `transformers` score was measured using `sacrebleu` on detokenized outputs.\n\nThe score was calculated using this code:\n\n```bash\ngit clone https://github.com/huggingface/transformers\ncd transformers\nexport PAIR={pair}\nexport DATA_DIR=data/$PAIR\nexport SAVE_DIR=data/$PAIR\nexport BS=8\nexport NUM_BEAMS=5\nmkdir -p $DATA_DIR\nsacrebleu -t wmt16 -l $PAIR --echo src > $DATA_DIR/val.source\nsacrebleu -t wmt16 -l $PAIR --echo ref > $DATA_DIR/val.target\necho $PAIR\nPYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval.py allenai/{model_name} $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation --num_beams $NUM_BEAMS\n```\n\n## Data Sources\n\n- [training, etc.](http://www.statmt.org/wmt16/)\n- [test set](http://matrix.statmt.org/test_sets/newstest2016.tgz?1504722372)\n\n\n### BibTeX entry and citation info\n\n```\n@misc{{kasai2020deep,\n    title={{Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation}},\n    author={{Jungo Kasai and Nikolaos Pappas and Hao Peng and James Cross and Noah A. Smith}},\n    year={{2020}},\n    eprint={{2006.10369}},\n    archivePrefix={{arXiv}},\n    primaryClass={{cs.CL}}\n}}\n```\n\n'
	model_card_dir.mkdir(parents=__snake_case ,exist_ok=__snake_case							)
	lowerCamelCase__              =	os.path.join(__snake_case ,'''README.md'''							)
	print(F'Generating {path}'							)
	with open(__snake_case ,'''w''' ,encoding='''utf-8'''							) as f:
		f.write(__snake_case							)


# make sure we are under the root of the project
_a						=   Path(__file__).resolve().parent.parent.parent
_a						=   repo_dir / "model_cards"

for model_name in ["wmt16-en-de-dist-12-1", "wmt16-en-de-dist-6-1", "wmt16-en-de-12-1"]:
			_a						=   model_cards_dir / "allenai" / model_name
			write_model_card(model_card_dir, src_lang="en", tgt_lang="de", model_name=model_name)



