



from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


UpperCAmelCase							 =					logging.get_logger(__name__)  # pylint: disable=invalid-name

UpperCAmelCase							 =					"""
    Examples:
        ```py
        >>> from diffusers import KandinskyV22Img2ImgPipeline, KandinskyV22PriorPipeline
        >>> from diffusers.utils import load_image
        >>> import torch

        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16
        ... )
        >>> pipe_prior.to(\"cuda\")

        >>> prompt = \"A red cartoon frog, 4k\"
        >>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)

        >>> pipe = KandinskyV22Img2ImgPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-decoder\", torch_dtype=torch.float16
        ... )
        >>> pipe.to(\"cuda\")

        >>> init_image = load_image(
        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"
        ...     \"/kandinsky/frog.png\"
        ... )

        >>> image = pipe(
        ...     image=init_image,
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     height=768,
        ...     width=768,
        ...     num_inference_steps=100,
        ...     strength=0.2,
        ... ).images

        >>> image[0].save(\"red_frog.png\")
        ```
"""

def 			A_      (	__a     :				Tuple ,		__a     :				Optional[Any] ,		__a     :				int=8		):

			"""simple docstring"""
			a__				      =		height // scale_factor**2
			if height % scale_factor**2 != 0:
						new_height += 1
			a__				      =		width // scale_factor**2
			if width % scale_factor**2 != 0:
						new_width += 1
			return new_height * scale_factor, new_width * scale_factor

def 			A_      (	__a     :				List[str] ,		__a     :				Any=512 ,		__a     :				List[Any]=512		):

			"""simple docstring"""
			a__				      =		pil_image.resize((w, h) ,		resample=Image.BICUBIC ,		reducing_gap=1		)
			a__				      =		np.array(pil_image.convert("""RGB"""		)		)
			a__				      =		arr.astype(np.floataa		) / 127.5 - 1
			a__				      =		np.transpose(__a ,		[2, 0, 1]		)
			a__				      =		torch.from_numpy(__a		).unsqueeze(0		)
			return image




class  __snake_case      (   SCREAMING_SNAKE_CASE):







							'''simple docstring'''






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

										self.register_modules(
										    unet=a_  ,  scheduler=a_  ,  movq=a_  ,  )
										a__				      =		2 ** (len(self.movq.config.block_out_channels		) - 1)


							def _a      (							self  ,  a_  ,  a_  ,  a_		):
										# get the original timestep using init_timestep
										a__				      =		min(int(num_inference_steps * strength		)  ,  a_		)

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

										return timesteps, num_inference_steps - t_start


							def _a      (							self  ,  a_  ,  a_  ,  a_  ,  a_  ,  a_  ,  a_  ,  a_=None		):
										if not isinstance(a_  ,  (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(a_		)}'''		)

										a__				      =		image.to(device=a_  ,  dtype=a_		)

										a__				      =		batch_size * num_images_per_prompt

										if image.shape[1] == 4:
													a__				      =		image

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

													elif isinstance(a_  ,  a_		):
																a__				      =		[
																    self.movq.encode(image[i : i + 1]		).latent_dist.sample(generator[i]		) for i in range(a_		)
																]
																a__				      =		torch.cat(a_  ,  dim=0		)
													else:
																a__				      =		self.movq.encode(a_		).latent_dist.sample(a_		)

													a__				      =		self.movq.config.scaling_factor * init_latents

										a__				      =		torch.cat([init_latents]  ,  dim=0		)

										a__				      =		init_latents.shape
										a__				      =		randn_tensor(a_  ,  generator=a_  ,  device=a_  ,  dtype=a_		)

										# get latents
										a__				      =		self.scheduler.add_noise(a_  ,  a_  ,  a_		)

										a__				      =		init_latents

										return latents


							def _a      (							self  ,  a_=0		):
										if is_accelerate_available():
													from accelerate import cpu_offload
										else:
													raise ImportError("""Please install accelerate via `pip install accelerate`"""		)

										a__				      =		torch.device(F'''cuda:{gpu_id}'''		)

										a__				      =		[
										    self.unet,
										    self.movq,
										]
										for cpu_offloaded_model in models:
													if cpu_offloaded_model is not None:
																cpu_offload(a_  ,  a_		)


							def _a      (							self  ,  a_=0		):
										if is_accelerate_available() and is_accelerate_version(""">="""  ,  """0.17.0.dev0"""		):
													from accelerate import cpu_offload_with_hook
										else:
													raise ImportError("""`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher."""		)

										a__				      =		torch.device(F'''cuda:{gpu_id}'''		)

										if self.device.type != "cpu":
													self.to("""cpu"""  ,  silence_dtype_warnings=a_		)
													torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

										a__				      =		None
										for cpu_offloaded_model in [self.unet, self.movq]:
													a__      ,			a__				      =		cpu_offload_with_hook(a_  ,  a_  ,  prev_module_hook=a_		)

										# We'll offload the last model manually.
										a__				      =		hook


							@property
							# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
							def _a      (							self		):
										if not hasattr(self.unet  ,  """_hf_hook"""		):
													return self.device
										for module in self.unet.modules():
													if (
													    hasattr(a_  ,  """_hf_hook"""		)
													    and hasattr(module._hf_hook  ,  """execution_device"""		)
													    and module._hf_hook.execution_device is not None
													):
																return torch.device(module._hf_hook.execution_device		)
										return self.device







							@torch.no_grad()
							@replace_example_docstring(a_		)
							def __call__(							self  ,  a_  ,  a_  ,  a_  ,  a_ = 512  ,  a_ = 512  ,  a_ = 100  ,  a_ = 4.0  ,  a_ = 0.3  ,  a_ = 1  ,  a_ = None  ,  a_ = "pil"  ,  a_ = True  ,  ):
										a__				      =		self._execution_device

										a__				      =		guidance_scale > 1.0

										if isinstance(a_  ,  a_		):
													a__				      =		torch.cat(a_  ,  dim=0		)
										a__				      =		image_embeds.shape[0]
										if isinstance(a_  ,  a_		):
													a__				      =		torch.cat(a_  ,  dim=0		)

										if do_classifier_free_guidance:
													a__				      =		image_embeds.repeat_interleave(a_  ,  dim=0		)
													a__				      =		negative_image_embeds.repeat_interleave(a_  ,  dim=0		)

										a__				      =		torch.cat([negative_image_embeds, image_embeds]  ,  dim=0		).to(dtype=self.unet.dtype  ,  device=a_		)

										if not isinstance(a_  ,  a_		):
													a__				      =		[image]
										if not all(isinstance(a_  ,  (PIL.Image.Image, torch.Tensor)		) for i in image		):
													raise ValueError(
													    F'''Input is in incorrect format: {[type(a_		) for i in image]}. Currently, we only support  PIL image and pytorch tensor'''		)

										a__				      =		torch.cat([prepare_image(a_  ,  a_  ,  a_		) for i in image]  ,  dim=0		)
										a__				      =		image.to(dtype=image_embeds.dtype  ,  device=a_		)

										a__				      =		self.movq.encode(a_		)["""latents"""]
										a__				      =		latents.repeat_interleave(a_  ,  dim=0		)
										self.scheduler.set_timesteps(a_  ,  device=a_		)
										a__      ,			a__				      =		self.get_timesteps(a_  ,  a_  ,  a_		)
										a__				      =		timesteps[:1].repeat(batch_size * num_images_per_prompt		)
										a__      ,			a__				      =		downscale_height_and_width(a_  ,  a_  ,  self.movq_scale_factor		)
										a__				      =		self.prepare_latents(
										    a_  ,  a_  ,  a_  ,  a_  ,  image_embeds.dtype  ,  a_  ,  a_		)
										for i, t in enumerate(self.progress_bar(a_		)		):
													# expand the latents if we are doing classifier free guidance
													a__				      =		torch.cat([latents] * 2		) if do_classifier_free_guidance else latents

													a__				      =		{"""image_embeds""": image_embeds}
													a__				      =		self.unet(
													    sample=a_  ,  timestep=a_  ,  encoder_hidden_states=a_  ,  added_cond_kwargs=a_  ,  return_dict=a_  ,  )[0]

													if do_classifier_free_guidance:
																a__      ,			a__				      =		noise_pred.split(latents.shape[1]  ,  dim=1		)
																a__      ,			a__				      =		noise_pred.chunk(2		)
																a__      ,			a__				      =		variance_pred.chunk(2		)
																a__				      =		noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
																a__				      =		torch.cat([noise_pred, variance_pred_text]  ,  dim=1		)

													if not (
													    hasattr(self.scheduler.config  ,  """variance_type"""		)
													    and self.scheduler.config.variance_type in ["learned", "learned_range"]
													):
																a__      ,			a__				      =		noise_pred.split(latents.shape[1]  ,  dim=1		)

													# compute the previous noisy sample x_t -> x_t-1
													a__				      =		self.scheduler.step(
													    a_  ,  a_  ,  a_  ,  generator=a_  ,  )[0]

										# post-processing
										a__				      =		self.movq.decode(a_  ,  force_not_quantize=a_		)["""sample"""]

										if output_type not in ["pt", "np", "pil"]:
													raise ValueError(F'''Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}'''		)

										if output_type in ["np", "pil"]:
													a__				      =		image * 0.5 + 0.5
													a__				      =		image.clamp(0  ,  1		)
													a__				      =		image.cpu().permute(0  ,  2  ,  3  ,  1		).float().numpy()

										if output_type == "pil":
													a__				      =		self.numpy_to_pil(a_		)

										if not return_dict:
													return (image,)

										return ImagePipelineOutput(images=a_		)






# Usage:
# ./gen-card-facebook-wmt19.py

import os
from pathlib import Path

def 			A_      (	__a     :				List[Any] ,		__a     :				List[str] ,		__a     :				Union[str, Any]		):

				"""simple docstring"""
				a__				      =		{
				    """en""": """Machine learning is great, isn't it?""",
				    """ru""": """Машинное обучение - это здорово, не так ли?""",
				    """de""": """Maschinelles Lernen ist großartig, oder?""",
				}

				# BLUE scores as follows:
				# "pair": [fairseq, transformers]
				a__				      =		{
				    """ru-en""": ["""[41.3](http://matrix.statmt.org/matrix/output/1907?run_id=6937)""", """39.20"""],
				    """en-ru""": ["""[36.4](http://matrix.statmt.org/matrix/output/1914?run_id=6724)""", """33.47"""],
				    """en-de""": ["""[43.1](http://matrix.statmt.org/matrix/output/1909?run_id=6862)""", """42.83"""],
				    """de-en""": ["""[42.3](http://matrix.statmt.org/matrix/output/1902?run_id=6750)""", """41.35"""],
				}
				a__				      =		F'''{src_lang}-{tgt_lang}'''

				a__				      =		F'''
---
language: 
- {src_lang}
- {tgt_lang}
thumbnail:
tags:
- translation
- wmt19
- facebook
license: apache-2.0
datasets:
- wmt19
metrics:
- bleu
---

# FSMT

## Model description

This is a ported version of [fairseq wmt19 transformer](https://github.com/pytorch/fairseq/blob/master/examples/wmt19/README.md) for {src_lang}-{tgt_lang}.

For more details, please see, [Facebook FAIR\'s WMT19 News Translation Task Submission](https://arxiv.org/abs/1907.06616).

The abbreviation FSMT stands for FairSeqMachineTranslation

All four models are available:

* [wmt19-en-ru](https://huggingface.co/facebook/wmt19-en-ru)
* [wmt19-ru-en](https://huggingface.co/facebook/wmt19-ru-en)
* [wmt19-en-de](https://huggingface.co/facebook/wmt19-en-de)
* [wmt19-de-en](https://huggingface.co/facebook/wmt19-de-en)

## Intended uses & limitations

#### How to use

```python
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
mname = "facebook/wmt19-{src_lang}-{tgt_lang}"
tokenizer = FSMTTokenizer.from_pretrained(mname)
model = FSMTForConditionalGeneration.from_pretrained(mname)

input = "{texts[src_lang]}"
input_ids = tokenizer.encode(input, return_tensors="pt")
outputs = model.generate(input_ids)
decoded = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(decoded) # {texts[tgt_lang]}

```

#### Limitations and bias

- The original (and this ported model) doesn\'t seem to handle well inputs with repeated sub-phrases, [content gets truncated](https://discuss.huggingface.co/t/issues-with-translating-inputs-containing-repeated-phrases/981)

## Training data

Pretrained weights were left identical to the original model released by fairseq. For more details, please, see the [paper](https://arxiv.org/abs/1907.06616).

## Eval results

pair   | fairseq | transformers
-------|---------|----------
{pair}  | {scores[pair][0]} | {scores[pair][1]}

The score is slightly below the score reported by `fairseq`, since `transformers`` currently doesn\'t support:
- model ensemble, therefore the best performing checkpoint was ported (``model4.pt``).
- re-ranking

The score was calculated using this code:

```bash
git clone https://github.com/huggingface/transformers
cd transformers
export PAIR={pair}
export DATA_DIR=data/$PAIR
export SAVE_DIR=data/$PAIR
export BS=8
export NUM_BEAMS=15
mkdir -p $DATA_DIR
sacrebleu -t wmt19 -l $PAIR --echo src > $DATA_DIR/val.source
sacrebleu -t wmt19 -l $PAIR --echo ref > $DATA_DIR/val.target
echo $PAIR
PYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval.py facebook/wmt19-$PAIR $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
```
note: fairseq reports using a beam of 50, so you should get a slightly higher score if re-run with `--num_beams 50`.

## Data Sources

- [training, etc.](http://www.statmt.org/wmt19/)
- [test set](http://matrix.statmt.org/test_sets/newstest2019.tgz?1556572561)


### BibTeX entry and citation info

```bibtex
@inproceedings{{...,
  year={{2020}},
  title={{Facebook FAIR\'s WMT19 News Translation Task Submission}},
  author={{Ng, Nathan and Yee, Kyra and Baevski, Alexei and Ott, Myle and Auli, Michael and Edunov, Sergey}},
  booktitle={{Proc. of WMT}},
}}
```


## TODO

- port model ensemble (fairseq uses 4 model checkpoints)

'''
				os.makedirs(__a ,		exist_ok=__a		)
				a__				      =		os.path.join(__a ,		"""README.md"""		)
				print(F'''Generating {path}'''		)
				with open(__a ,		"""w""" ,		encoding="""utf-8"""		) as f:
								f.write(__a		)


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

for model_name in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
	UpperCAmelCase ,       UpperCAmelCase ,       UpperCAmelCase							 =					model_name.split("""-""")
	UpperCAmelCase							 =					model_cards_dir / """facebook""" / model_name
	write_model_card(model_card_dir, src_lang=src_lang, tgt_lang=tgt_lang)

