



'''simple docstring'''



import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List

import git
import torch
from torch.utils.data import Dataset

from transformers import BartTokenizer, RagTokenizer, TaTokenizer



def   __lowerCAmelCase					(							lowerCamelCase   :						str				,		lowerCamelCase   :						List[Any]				,		lowerCamelCase   :						Dict				,		lowerCamelCase   :						Tuple				,		lowerCamelCase   :						Optional[Any]=True				,		lowerCamelCase   :						str="pt"					):






	'''simple docstring'''




	__lowerCAmelCase												=  {"add_prefix_space": True} if isinstance(lowerCamelCase				,		lowerCamelCase					) and not line.startswith(" "					) else {}
	__lowerCAmelCase												=  padding_side
	return tokenizer(
	    [line]				,		max_length=lowerCamelCase				,		padding="max_length" if pad_to_max_length else None				,		truncation=lowerCamelCase				,		return_tensors=lowerCamelCase				,		add_special_tokens=lowerCamelCase				,		**lowerCamelCase				,		)



def   __lowerCAmelCase					(							lowerCamelCase   :						int				,		lowerCamelCase   :						Tuple				,		lowerCamelCase   :						Tuple=None				,		):






	'''simple docstring'''




	__lowerCAmelCase												=  input_ids.ne(lowerCamelCase					).any(dim=0					)
	if attention_mask is None:
		return input_ids[:, keep_column_mask]
	else:
		return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])






class 					UpperCAmelCase__	(		UpperCamelCase__ ):



					def __init__(    self			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase="train"			,				UpperCamelCase=None			,				UpperCamelCase=None			,				UpperCamelCase=None			,				UpperCamelCase=""			,				)     ->			Union[str, Any]:
						super().__init__()
						__lowerCAmelCase												=  Path(UpperCamelCase     ).joinpath(type_path + ".source"     )
						__lowerCAmelCase												=  Path(UpperCamelCase     ).joinpath(type_path + ".target"     )
						__lowerCAmelCase												=  self.get_char_lens(self.src_file     )
						__lowerCAmelCase												=  max_source_length
						__lowerCAmelCase												=  max_target_length
						assert min(self.src_lens     ) > 0, F'''found empty line in {self.src_file}'''
						__lowerCAmelCase												=  tokenizer
						__lowerCAmelCase												=  prefix
						if n_obs is not None:
							__lowerCAmelCase												=  self.src_lens[:n_obs]
						__lowerCAmelCase												=  src_lang
						__lowerCAmelCase												=  tgt_lang



					def __len__(    self     )     ->			List[str]:
						return len(self.src_lens     )



					def __getitem__(    self			,				UpperCamelCase     )     ->			Dict[str, torch.Tensor]:
						__lowerCAmelCase												=  index + 1  # linecache starts at 1
						__lowerCAmelCase												=  self.prefix + linecache.getline(str(self.src_file     )			,				UpperCamelCase     ).rstrip("\n"     )
						__lowerCAmelCase												=  linecache.getline(str(self.tgt_file     )			,				UpperCamelCase     ).rstrip("\n"     )
						assert source_line, F'''empty source line for index {index}'''
						assert tgt_line, F'''empty tgt line for index {index}'''

						# Need to add eos token manually for T5
						if isinstance(self.tokenizer			,				UpperCamelCase     ):
							source_line += self.tokenizer.eos_token
							tgt_line += self.tokenizer.eos_token

						# Pad source and target to the right
						__lowerCAmelCase												=  (
						    self.tokenizer.question_encoder if isinstance(self.tokenizer			,				UpperCamelCase     ) else self.tokenizer
						)
						__lowerCAmelCase												=  self.tokenizer.generator if isinstance(self.tokenizer			,				UpperCamelCase     ) else self.tokenizer

						__lowerCAmelCase												=  encode_line(UpperCamelCase			,				UpperCamelCase			,				self.max_source_length			,				"right"     )
						__lowerCAmelCase												=  encode_line(UpperCamelCase			,				UpperCamelCase			,				self.max_target_length			,				"right"     )

						__lowerCAmelCase												=  source_inputs["input_ids"].squeeze()
						__lowerCAmelCase												=  target_inputs["input_ids"].squeeze()
						__lowerCAmelCase												=  source_inputs["attention_mask"].squeeze()
						return {
						    "input_ids": source_ids,
						    "attention_mask": src_mask,
						    "decoder_input_ids": target_ids,
						}



					@staticmethod
					def        UpperCAmelCase_ (    UpperCamelCase     )     ->			List[Any]:
						return [len(UpperCamelCase     ) for x in Path(UpperCamelCase     ).open().readlines()]



					def        UpperCAmelCase_ (    self			,				UpperCamelCase     )     ->			Dict[str, torch.Tensor]:
						__lowerCAmelCase												=  torch.stack([x["input_ids"] for x in batch]     )
						__lowerCAmelCase												=  torch.stack([x["attention_mask"] for x in batch]     )
						__lowerCAmelCase												=  torch.stack([x["decoder_input_ids"] for x in batch]     )
						__lowerCAmelCase												=  (
						    self.tokenizer.generator.pad_token_id
						    if isinstance(self.tokenizer			,				UpperCamelCase     )
						    else self.tokenizer.pad_token_id
						)
						__lowerCAmelCase												=  (
						    self.tokenizer.question_encoder.pad_token_id
						    if isinstance(self.tokenizer			,				UpperCamelCase     )
						    else self.tokenizer.pad_token_id
						)
						__lowerCAmelCase												=  trim_batch(UpperCamelCase			,				UpperCamelCase     )
						__lowerCAmelCase							,       __lowerCAmelCase												=  trim_batch(UpperCamelCase			,				UpperCamelCase			,				attention_mask=UpperCamelCase     )
						__lowerCAmelCase												=  {
						    "input_ids": source_ids,
						    "attention_mask": source_mask,
						    "decoder_input_ids": y,
						}
						return batch


lowerCAmelCase	:		List[Any]				=   getLogger(__name__)



def   __lowerCAmelCase					(							lowerCamelCase   :						List[List]					):






	'''simple docstring'''




	return list(itertools.chain.from_iterable(lowerCamelCase					)					)



def   __lowerCAmelCase					(							lowerCamelCase   :						str					):






	'''simple docstring'''




	__lowerCAmelCase												=  get_git_info()
	save_json(lowerCamelCase				,		os.path.join(lowerCamelCase				,		"git_log.json"					)					)



def   __lowerCAmelCase					(							lowerCamelCase   :						Any				,		lowerCamelCase   :						List[Any]				,		lowerCamelCase   :						Optional[Any]=4				,		**lowerCamelCase   :						Union[str, Any]					):






	'''simple docstring'''




	with open(lowerCamelCase				,		"w"					) as f:
		json.dump(lowerCamelCase				,		lowerCamelCase				,		indent=lowerCamelCase				,		**lowerCamelCase					)



def   __lowerCAmelCase					(							lowerCamelCase   :						Tuple					):






	'''simple docstring'''




	with open(lowerCamelCase					) as f:
		return json.load(lowerCamelCase					)



def   __lowerCAmelCase					(							):






	'''simple docstring'''




	__lowerCAmelCase												=  git.Repo(search_parent_directories=lowerCamelCase					)
	__lowerCAmelCase												=  {
	    "repo_id": str(lowerCamelCase					),
	    "repo_sha": str(repo.head.object.hexsha					),
	    "repo_branch": str(repo.active_branch					),
	    "hostname": str(socket.gethostname()					),
	}
	return repo_infos



def   __lowerCAmelCase					(							lowerCamelCase   :						Callable				,		lowerCamelCase   :						Iterable					):






	'''simple docstring'''




	return list(map(lowerCamelCase				,		lowerCamelCase					)					)



def   __lowerCAmelCase					(							lowerCamelCase   :						List[str]				,		lowerCamelCase   :						Dict					):






	'''simple docstring'''




	with open(lowerCamelCase				,		"wb"					) as f:
		return pickle.dump(lowerCamelCase				,		lowerCamelCase					)



def   __lowerCAmelCase					(							lowerCamelCase   :						Union[str, Any]					):






	'''simple docstring'''




	def remove_articles(lowerCamelCase   :						str					):
		return re.sub(r"\b(a|an|the)\b"				,		" "				,		lowerCamelCase					)

	def white_space_fix(lowerCamelCase   :						int					):
		return " ".join(text.split()					)

	def remove_punc(lowerCamelCase   :						Tuple					):
		__lowerCAmelCase												=  set(string.punctuation					)
		return "".join(ch for ch in text if ch not in exclude					)

	def lower(lowerCamelCase   :						int					):
		return text.lower()

	return white_space_fix(remove_articles(remove_punc(lower(lowerCamelCase					)					)					)					)



def   __lowerCAmelCase					(							lowerCamelCase   :						Optional[Any]				,		lowerCamelCase   :						Union[str, Any]					):






	'''simple docstring'''




	__lowerCAmelCase												=  normalize_answer(lowerCamelCase					).split()
	__lowerCAmelCase												=  normalize_answer(lowerCamelCase					).split()
	__lowerCAmelCase												=  Counter(lowerCamelCase					) & Counter(lowerCamelCase					)
	__lowerCAmelCase												=  sum(common.values()					)
	if num_same == 0:
		return 0
	__lowerCAmelCase												=  1.0 * num_same / len(lowerCamelCase					)
	__lowerCAmelCase												=  1.0 * num_same / len(lowerCamelCase					)
	__lowerCAmelCase												=  (2 * precision * recall) / (precision + recall)
	return fa



def   __lowerCAmelCase					(							lowerCamelCase   :						Dict				,		lowerCamelCase   :						List[Any]					):






	'''simple docstring'''




	return normalize_answer(lowerCamelCase					) == normalize_answer(lowerCamelCase					)



def   __lowerCAmelCase					(							lowerCamelCase   :						List[str]				,		lowerCamelCase   :						List[str]					):






	'''simple docstring'''




	assert len(lowerCamelCase					) == len(lowerCamelCase					)
	__lowerCAmelCase												=  0
	for hypo, pred in zip(lowerCamelCase				,		lowerCamelCase					):
		em += exact_match_score(lowerCamelCase				,		lowerCamelCase					)
	if len(lowerCamelCase					) > 0:
		em /= len(lowerCamelCase					)
	return {"em": em}



def   __lowerCAmelCase					(							lowerCamelCase   :						Tuple					):






	'''simple docstring'''




	return model_prefix.startswith("rag"					)




def   __lowerCAmelCase					(							lowerCamelCase   :						Optional[int]				,		lowerCamelCase   :						str				,		lowerCamelCase   :						List[Any]					):






	'''simple docstring'''




	__lowerCAmelCase												=  {p: p for p in extra_params}
	# T5 models don't have `dropout` param, they have `dropout_rate` instead
	__lowerCAmelCase												=  "dropout_rate"
	for p in extra_params:
		if getattr(lowerCamelCase				,		lowerCamelCase				,		lowerCamelCase					):
			if not hasattr(lowerCamelCase				,		lowerCamelCase					) and not hasattr(lowerCamelCase				,		equivalent_param[p]					):
				logger.info("config doesn't have a `{}` attribute".format(lowerCamelCase					)					)
				delattr(lowerCamelCase				,		lowerCamelCase					)
				continue
			__lowerCAmelCase												=  p if hasattr(lowerCamelCase				,		lowerCamelCase					) else equivalent_param[p]
			setattr(lowerCamelCase				,		lowerCamelCase				,		getattr(lowerCamelCase				,		lowerCamelCase					)					)
			delattr(lowerCamelCase				,		lowerCamelCase					)
	return hparams, config




'''simple docstring'''



import gc
import random
import unittest

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

from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()






class 					UpperCAmelCase__	(		UpperCamelCase__							,       unittest.TestCase ):
					a      :     List[Any]        =							KandinskyImgaImgPipeline
					a      :     Union[str, Any]        =							["""prompt""", """image_embeds""", """negative_image_embeds""", """image"""]
					a      :     List[Any]        =							[
					    """prompt""",
					    """negative_prompt""",
					    """image_embeds""",
					    """negative_image_embeds""",
					    """image""",
					]
					a      :     Any        =							[
					    """generator""",
					    """height""",
					    """width""",
					    """strength""",
					    """guidance_scale""",
					    """negative_prompt""",
					    """num_inference_steps""",
					    """return_dict""",
					    """guidance_scale""",
					    """num_images_per_prompt""",
					    """output_type""",
					    """return_dict""",
					]
					a      :     Union[str, Any]        =							False



					@property
					def        UpperCAmelCase_ (    self     )     ->			int:
						return 32



					@property
					def        UpperCAmelCase_ (    self     )     ->			List[str]:
						return 32



					@property
					def        UpperCAmelCase_ (    self     )     ->			Dict:
						return self.time_input_dim



					@property
					def        UpperCAmelCase_ (    self     )     ->			int:
						return self.time_input_dim * 4



					@property
					def        UpperCAmelCase_ (    self     )     ->			int:
						return 100



					@property
					def        UpperCAmelCase_ (    self     )     ->			Optional[int]:
						__lowerCAmelCase												=  XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base"     )
						return tokenizer



					@property
					def        UpperCAmelCase_ (    self     )     ->			Union[str, Any]:
						torch.manual_seed(0     )
						__lowerCAmelCase												=  MCLIPConfig(
						    numDims=self.cross_attention_dim			,				transformerDimensions=self.text_embedder_hidden_size			,				hidden_size=self.text_embedder_hidden_size			,				intermediate_size=37			,				num_attention_heads=4			,				num_hidden_layers=5			,				vocab_size=1005			,				)

						__lowerCAmelCase												=  MultilingualCLIP(UpperCamelCase     )
						__lowerCAmelCase												=  text_encoder.eval()

						return text_encoder



					@property
					def        UpperCAmelCase_ (    self     )     ->			List[str]:
						torch.manual_seed(0     )

						__lowerCAmelCase												=  {
						    "in_channels": 4,
						    # Out channels is double in channels because predicts mean and variance
						    "out_channels": 8,
						    "addition_embed_type": "text_image",
						    "down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"),
						    "up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"),
						    "mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
						    "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
						    "layers_per_block": 1,
						    "encoder_hid_dim": self.text_embedder_hidden_size,
						    "encoder_hid_dim_type": "text_image_proj",
						    "cross_attention_dim": self.cross_attention_dim,
						    "attention_head_dim": 4,
						    "resnet_time_scale_shift": "scale_shift",
						    "class_embed_type": None,
						}

						__lowerCAmelCase												=  UNetaDConditionModel(**UpperCamelCase     )
						return model



					@property
					def        UpperCAmelCase_ (    self     )     ->			List[Any]:
						return {
						    "block_out_channels": [32, 64],
						    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
						    "in_channels": 3,
						    "latent_channels": 4,
						    "layers_per_block": 1,
						    "norm_num_groups": 8,
						    "norm_type": "spatial",
						    "num_vq_embeddings": 12,
						    "out_channels": 3,
						    "up_block_types": [
						        "AttnUpDecoderBlock2D",
						        "UpDecoderBlock2D",
						    ],
						    "vq_embed_dim": 4,
						}



					@property
					def        UpperCAmelCase_ (    self     )     ->			Dict:
						torch.manual_seed(0     )
						__lowerCAmelCase												=  VQModel(**self.dummy_movq_kwargs     )
						return model



					def        UpperCAmelCase_ (    self     )     ->			Any:
						__lowerCAmelCase												=  self.dummy_text_encoder
						__lowerCAmelCase												=  self.dummy_tokenizer
						__lowerCAmelCase												=  self.dummy_unet
						__lowerCAmelCase												=  self.dummy_movq

						__lowerCAmelCase												=  {
						    "num_train_timesteps": 1000,
						    "beta_schedule": "linear",
						    "beta_start": 0.0_00_85,
						    "beta_end": 0.0_12,
						    "clip_sample": False,
						    "set_alpha_to_one": False,
						    "steps_offset": 0,
						    "prediction_type": "epsilon",
						    "thresholding": False,
						}

						__lowerCAmelCase												=  DDIMScheduler(**UpperCamelCase     )

						__lowerCAmelCase												=  {
						    "text_encoder": text_encoder,
						    "tokenizer": tokenizer,
						    "unet": unet,
						    "scheduler": scheduler,
						    "movq": movq,
						}

						return components



					def        UpperCAmelCase_ (    self			,				UpperCamelCase			,				UpperCamelCase=0     )     ->			Optional[Any]:
						__lowerCAmelCase												=  floats_tensor((1, self.cross_attention_dim)			,				rng=random.Random(UpperCamelCase     )     ).to(UpperCamelCase     )
						__lowerCAmelCase												=  floats_tensor((1, self.cross_attention_dim)			,				rng=random.Random(seed + 1     )     ).to(UpperCamelCase     )
						# create init_image
						__lowerCAmelCase												=  floats_tensor((1, 3, 64, 64)			,				rng=random.Random(UpperCamelCase     )     ).to(UpperCamelCase     )
						__lowerCAmelCase												=  image.cpu().permute(0			,				2			,				3			,				1     )[0]
						__lowerCAmelCase												=  Image.fromarray(np.uinta(UpperCamelCase     )     ).convert("RGB"     ).resize((256, 256)     )

						if str(UpperCamelCase     ).startswith("mps"     ):
							__lowerCAmelCase												=  torch.manual_seed(UpperCamelCase     )
						else:
							__lowerCAmelCase												=  torch.Generator(device=UpperCamelCase     ).manual_seed(UpperCamelCase     )
						__lowerCAmelCase												=  {
						    "prompt": "horse",
						    "image": init_image,
						    "image_embeds": image_embeds,
						    "negative_image_embeds": negative_image_embeds,
						    "generator": generator,
						    "height": 64,
						    "width": 64,
						    "num_inference_steps": 10,
						    "guidance_scale": 7.0,
						    "strength": 0.2,
						    "output_type": "np",
						}
						return inputs



					def        UpperCAmelCase_ (    self     )     ->			Tuple:
						__lowerCAmelCase												=  "cpu"

						__lowerCAmelCase												=  self.get_dummy_components()

						__lowerCAmelCase												=  self.pipeline_class(**UpperCamelCase     )
						__lowerCAmelCase												=  pipe.to(UpperCamelCase     )

						pipe.set_progress_bar_config(disable=UpperCamelCase     )

						__lowerCAmelCase												=  pipe(**self.get_dummy_inputs(UpperCamelCase     )     )
						__lowerCAmelCase												=  output.images

						__lowerCAmelCase												=  pipe(
						    **self.get_dummy_inputs(UpperCamelCase     )			,				return_dict=UpperCamelCase			,				)[0]

						__lowerCAmelCase												=  image[0, -3:, -3:, -1]
						__lowerCAmelCase												=  image_from_tuple[0, -3:, -3:, -1]

						assert image.shape == (1, 64, 64, 3)

						__lowerCAmelCase												=  np.array(
						    [0.61_47_49_43, 0.6_07_35_39, 0.43_30_85_44, 0.5_92_82_69, 0.47_49_35_95, 0.46_75_59_73, 0.4_61_38_38, 0.45_36_87_97, 0.50_11_92_33]     )
						assert (
						    np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-2
						), F''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''
						assert (
						    np.abs(image_from_tuple_slice.flatten() - expected_slice     ).max() < 1E-2
						), F''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''






@slow
@require_torch_gpu
class 					UpperCAmelCase__	(		unittest.TestCase ):



					def        UpperCAmelCase_ (    self     )     ->			List[Any]:
						# clean up the VRAM after each test
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()



					def        UpperCAmelCase_ (    self     )     ->			List[Any]:
						__lowerCAmelCase												=  load_numpy(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
						    "/kandinsky/kandinsky_img2img_frog.npy"     )

						__lowerCAmelCase												=  load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png"     )
						__lowerCAmelCase												=  "A red cartoon frog, 4k"

						__lowerCAmelCase												=  KandinskyPriorPipeline.from_pretrained(
						    "kandinsky-community/kandinsky-2-1-prior"			,				torch_dtype=torch.floataa     )
						pipe_prior.to(UpperCamelCase     )

						__lowerCAmelCase												=  KandinskyImgaImgPipeline.from_pretrained(
						    "kandinsky-community/kandinsky-2-1"			,				torch_dtype=torch.floataa     )
						__lowerCAmelCase												=  pipeline.to(UpperCamelCase     )

						pipeline.set_progress_bar_config(disable=UpperCamelCase     )

						__lowerCAmelCase												=  torch.Generator(device="cpu"     ).manual_seed(0     )
						__lowerCAmelCase							,       __lowerCAmelCase												=  pipe_prior(
						    UpperCamelCase			,				generator=UpperCamelCase			,				num_inference_steps=5			,				negative_prompt=""			,				).to_tuple()

						__lowerCAmelCase												=  pipeline(
						    UpperCamelCase			,				image=UpperCamelCase			,				image_embeds=UpperCamelCase			,				negative_image_embeds=UpperCamelCase			,				generator=UpperCamelCase			,				num_inference_steps=100			,				height=768			,				width=768			,				strength=0.2			,				output_type="np"			,				)

						__lowerCAmelCase												=  output.images[0]

						assert image.shape == (768, 768, 3)

						assert_mean_pixel_difference(UpperCamelCase			,				UpperCamelCase     )