



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 			UpperCamelCase(  __UpperCamelCase							:		Dict     ,__UpperCamelCase							:		Tuple     ,__UpperCamelCase							:		Optional[Any]     ,__UpperCamelCase							:		List[str]     ,__UpperCamelCase							:		Optional[Any]=True     ,__UpperCamelCase							:		Tuple="pt" ):
		lowerCAmelCase_			:     Optional[int]									= {'''add_prefix_space''': True} if isinstance(__UpperCamelCase     ,__UpperCamelCase ) and not line.startswith(''' ''' ) else {}
		lowerCAmelCase_			:     List[Any]									= padding_side
		return tokenizer(
		    [line]     ,max_length=__UpperCamelCase     ,padding='''max_length''' if pad_to_max_length else None     ,truncation=__UpperCamelCase     ,return_tensors=__UpperCamelCase     ,add_special_tokens=__UpperCamelCase     ,**__UpperCamelCase     ,)


def 			UpperCamelCase(  __UpperCamelCase							:		List[Any]     ,__UpperCamelCase							:		Optional[int]     ,__UpperCamelCase							:		Union[str, Any]=None     ,):
		lowerCAmelCase_			:     Any									= input_ids.ne(__UpperCamelCase ).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 				__snake_case						(     UpperCamelCase_						):




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




							def __len__(       self     :       str):
									return len(self.src_lens)




							def __getitem__(       self     :       List[Any]				,				A_     :       Union[str, Any]):
									lowerCAmelCase_			:     Dict									= index + 1  # linecache starts at 1
									lowerCAmelCase_			:     Union[str, Any]									= self.prefix + linecache.getline(str(self.src_file)				,				A_).rstrip('''\n''')
									lowerCAmelCase_			:     Optional[int]									= linecache.getline(str(self.tgt_file)				,				A_).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				,				A_):
											source_line += self.tokenizer.eos_token
											tgt_line += self.tokenizer.eos_token

									# Pad source and target to the right
									lowerCAmelCase_			:     Tuple									= (
									    self.tokenizer.question_encoder if isinstance(self.tokenizer				,				A_) else self.tokenizer
									)
									lowerCAmelCase_			:     Optional[Any]									= self.tokenizer.generator if isinstance(self.tokenizer				,				A_) else self.tokenizer

									lowerCAmelCase_			:     Any									= encode_line(A_				,				A_				,				self.max_source_length				,				'''right''')
									lowerCAmelCase_			:     List[str]									= encode_line(A_				,				A_				,				self.max_target_length				,				'''right''')

									lowerCAmelCase_			:     List[str]									= source_inputs['''input_ids'''].squeeze()
									lowerCAmelCase_			:     Optional[int]									= target_inputs['''input_ids'''].squeeze()
									lowerCAmelCase_			:     List[str]									= source_inputs['''attention_mask'''].squeeze()
									return {
									    "input_ids": source_ids,
									    "attention_mask": src_mask,
									    "decoder_input_ids": target_ids,
									}




							@staticmethod
							def    UpperCAmelCase__			(       A_     :       List[str]):
									return [len(A_) for x in Path(A_).open().readlines()]







							def    UpperCAmelCase__			(       self     :       str				,				A_     :       int):
									lowerCAmelCase_			:     List[Any]									= torch.stack([x['''input_ids'''] for x in batch])
									lowerCAmelCase_			:     List[Any]									= torch.stack([x['''attention_mask'''] for x in batch])
									lowerCAmelCase_			:     Optional[Any]									= torch.stack([x['''decoder_input_ids'''] for x in batch])
									lowerCAmelCase_			:     Optional[Any]									= (
									    self.tokenizer.generator.pad_token_id
									    if isinstance(self.tokenizer				,				A_)
									    else self.tokenizer.pad_token_id
									)
									lowerCAmelCase_			:     List[str]									= (
									    self.tokenizer.question_encoder.pad_token_id
									    if isinstance(self.tokenizer				,				A_)
									    else self.tokenizer.pad_token_id
									)
									lowerCAmelCase_			:     Any									= trim_batch(A_				,				A_)
									lowerCAmelCase_					,  lowerCAmelCase_			:     Dict									= trim_batch(A_				,				A_				,				attention_mask=A_)
									lowerCAmelCase_			:     Dict									= {
									    '''input_ids''': source_ids,
									    '''attention_mask''': source_mask,
									    '''decoder_input_ids''': y,
									}
									return batch





A__  :  Optional[int]								=					getLogger(__name__)


def 			UpperCamelCase(  __UpperCamelCase							:		List[List] ):
		return list(itertools.chain.from_iterable(__UpperCamelCase ) )


def 			UpperCamelCase(  __UpperCamelCase							:		str ):
		lowerCAmelCase_			:     Optional[int]									= get_git_info()
		save_json(__UpperCamelCase     ,os.path.join(__UpperCamelCase     ,'''git_log.json''' ) )


def 			UpperCamelCase(  __UpperCamelCase							:		str     ,__UpperCamelCase							:		Optional[Any]     ,__UpperCamelCase							:		str=4     ,**__UpperCamelCase							:		Tuple ):
		with open(__UpperCamelCase     ,'''w''' ) as f:
				json.dump(__UpperCamelCase     ,__UpperCamelCase     ,indent=__UpperCamelCase     ,**__UpperCamelCase )


def 			UpperCamelCase(  __UpperCamelCase							:		List[Any] ):
		with open(__UpperCamelCase ) as f:
				return json.load(__UpperCamelCase )


def 			UpperCamelCase(  ):
		lowerCAmelCase_			:     Dict									= git.Repo(search_parent_directories=__UpperCamelCase )
		lowerCAmelCase_			:     Union[str, Any]									= {
		    '''repo_id''': str(__UpperCamelCase ),
		    '''repo_sha''': str(repo.head.object.hexsha ),
		    '''repo_branch''': str(repo.active_branch ),
		    '''hostname''': str(socket.gethostname() ),
		}
		return repo_infos


def 			UpperCamelCase(  __UpperCamelCase							:		Callable     ,__UpperCamelCase							:		Iterable ):
		return list(map(__UpperCamelCase     ,__UpperCamelCase ) )


def 			UpperCamelCase(  __UpperCamelCase							:		Optional[int]     ,__UpperCamelCase							:		List[Any] ):
		with open(__UpperCamelCase     ,'''wb''' ) as f:
				return pickle.dump(__UpperCamelCase     ,__UpperCamelCase )


def 			UpperCamelCase(  __UpperCamelCase							:		Optional[int] ):

		def remove_articles(__UpperCamelCase							:		List[str] ):
				return re.sub(R'''\b(a|an|the)\b'''     ,''' '''     ,__UpperCamelCase )

		def white_space_fix(__UpperCamelCase							:		Optional[Any] ):
				return " ".join(text.split() )

		def remove_punc(__UpperCamelCase							:		int ):
				lowerCAmelCase_			:     Optional[Any]									= set(string.punctuation )
				return "".join(ch for ch in text if ch not in exclude )

		def lower(__UpperCamelCase							:		Union[str, Any] ):
				return text.lower()

		return white_space_fix(remove_articles(remove_punc(lower(__UpperCamelCase ) ) ) )


def 			UpperCamelCase(  __UpperCamelCase							:		Tuple     ,__UpperCamelCase							:		List[Any] ):
		lowerCAmelCase_			:     int									= normalize_answer(__UpperCamelCase ).split()
		lowerCAmelCase_			:     Dict									= normalize_answer(__UpperCamelCase ).split()
		lowerCAmelCase_			:     Dict									= Counter(__UpperCamelCase ) & Counter(__UpperCamelCase )
		lowerCAmelCase_			:     List[str]									= sum(common.values() )
		if num_same == 0:
				return 0
		lowerCAmelCase_			:     str									= 1.0 * num_same / len(__UpperCamelCase )
		lowerCAmelCase_			:     Union[str, Any]									= 1.0 * num_same / len(__UpperCamelCase )
		lowerCAmelCase_			:     Optional[Any]									= (2 * precision * recall) / (precision + recall)
		return fa


def 			UpperCamelCase(  __UpperCamelCase							:		int     ,__UpperCamelCase							:		Tuple ):
		return normalize_answer(__UpperCamelCase ) == normalize_answer(__UpperCamelCase )


def 			UpperCamelCase(  __UpperCamelCase							:		List[str]     ,__UpperCamelCase							:		List[str] ):
		assert len(__UpperCamelCase ) == len(__UpperCamelCase )
		lowerCAmelCase_			:     Union[str, Any]									= 0
		for hypo, pred in zip(__UpperCamelCase     ,__UpperCamelCase ):
				em += exact_match_score(__UpperCamelCase     ,__UpperCamelCase )
		if len(__UpperCamelCase ) > 0:
				em /= len(__UpperCamelCase )
		return {"em": em}


def 			UpperCamelCase(  __UpperCamelCase							:		Union[str, Any] ):
		return model_prefix.startswith('''rag''' )





def 			UpperCamelCase(  __UpperCamelCase							:		int     ,__UpperCamelCase							:		str     ,__UpperCamelCase							:		Any ):
		lowerCAmelCase_			:     Dict									= {p: p for p in extra_params}
		# T5 models don't have `dropout` param, they have `dropout_rate` instead
		lowerCAmelCase_			:     Optional[int]									= '''dropout_rate'''
		for p in extra_params:
				if getattr(__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase ):
						if not hasattr(__UpperCamelCase     ,__UpperCamelCase ) and not hasattr(__UpperCamelCase     ,equivalent_param[p] ):
								logger.info('''config doesn\'t have a `{}` attribute'''.format(__UpperCamelCase ) )
								delattr(__UpperCamelCase     ,__UpperCamelCase )
								continue
						lowerCAmelCase_			:     List[str]									= p if hasattr(__UpperCamelCase     ,__UpperCamelCase ) else equivalent_param[p]
						setattr(__UpperCamelCase     ,__UpperCamelCase     ,getattr(__UpperCamelCase     ,__UpperCamelCase ) )
						delattr(__UpperCamelCase     ,__UpperCamelCase )
		return hparams, config










import gc
import unittest

from diffusers import FlaxStableDiffusionInpaintPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
						import jax
						import jax.numpy as jnp
						from flax.jax_utils import replicate
						from flax.training.common_utils import shard



@slow
@require_flax
class 				__snake_case						(     unittest.TestCase						):




							def    UpperCAmelCase__			(       self     :       Tuple):
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()







							def    UpperCAmelCase__			(       self     :       List[str]):
									lowerCAmelCase_			:     Any									= load_image(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
									    '''/sd2-inpaint/init_image.png''')
									lowerCAmelCase_			:     str									= load_image(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png''')

									lowerCAmelCase_			:     List[Any]									= '''xvjiarui/stable-diffusion-2-inpainting'''
									lowerCAmelCase_					,  lowerCAmelCase_			:     Optional[Any]									= FlaxStableDiffusionInpaintPipeline.from_pretrained(A_				,				safety_checker=A_)

									lowerCAmelCase_			:     List[str]									= '''Face of a yellow cat, high resolution, sitting on a park bench'''

									lowerCAmelCase_			:     List[Any]									= jax.random.PRNGKey(0)
									lowerCAmelCase_			:     str									= 5_0

									lowerCAmelCase_			:     List[Any]									= jax.device_count()
									lowerCAmelCase_			:     Union[str, Any]									= num_samples * [prompt]
									lowerCAmelCase_			:     str									= num_samples * [init_image]
									lowerCAmelCase_			:     Union[str, Any]									= num_samples * [mask_image]
									lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_			:     Optional[Any]									= pipeline.prepare_inputs(A_				,				A_				,				A_)

									# shard inputs and rng
									lowerCAmelCase_			:     str									= replicate(A_)
									lowerCAmelCase_			:     str									= jax.random.split(A_				,				jax.device_count())
									lowerCAmelCase_			:     List[Any]									= shard(A_)
									lowerCAmelCase_			:     str									= shard(A_)
									lowerCAmelCase_			:     Tuple									= shard(A_)

									lowerCAmelCase_			:     int									= pipeline(
									    A_				,				A_				,				A_				,				A_				,				A_				,				A_				,				jit=A_)

									lowerCAmelCase_			:     Optional[int]									= output.images.reshape(A_				,				5_1_2				,				5_1_2				,				3)

									lowerCAmelCase_			:     List[str]									= images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

									lowerCAmelCase_			:     int									= jnp.asarray(jax.device_get(image_slice.flatten()))
									lowerCAmelCase_			:     str									= jnp.array(
									    [0.361_1307, 0.3764_9736, 0.375_7408, 0.3821_3953, 0.3929_5167, 0.384_1631, 0.4155_4978, 0.413_7475, 0.421_7084])
									print(F"""output_slice: {output_slice}""")
									assert jnp.abs(output_slice - expected_slice).max() < 1e-2





