"""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 							_UpperCAmelCase		(       lowerCamelCase__			,   lowerCamelCase__			,   lowerCamelCase__			,   lowerCamelCase__			,   lowerCamelCase__=True			,   lowerCamelCase__="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 							_UpperCAmelCase		(       lowerCamelCase__			,   lowerCamelCase__			,   lowerCamelCase__=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       a_      (     __UpperCamelCase			):




					def __init__(	self						:     Union[str, Any]  ,						snake_case__						:     Tuple  ,						snake_case__						:     Tuple  ,						snake_case__						:     List[str]  ,						snake_case__						:     Any  ,						snake_case__						:     Optional[int]="train"  ,						snake_case__						:     Union[str, Any]=None  ,						snake_case__						:     List[Any]=None  ,						snake_case__						:     Union[str, Any]=None  ,						snake_case__						:     Optional[Any]=""  ,						):
							super().__init__()
							lowerCAmelCase__        =   Path(snake_case__     ).joinpath(type_path + """.source"""     )
							lowerCAmelCase__        =   Path(snake_case__     ).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						:     Tuple     ):
							return len(self.src_lens     )




					def __getitem__(	self						:     Any  ,						snake_case__						:     Union[str, Any]     ):
							lowerCAmelCase__        =   index + 1  # linecache starts at 1
							lowerCAmelCase__        =   self.prefix + linecache.getline(str(self.src_file     )  ,						snake_case__     ).rstrip("""\n"""     )
							lowerCAmelCase__        =   linecache.getline(str(self.tgt_file     )  ,						snake_case__     ).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  ,						snake_case__     ):
									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  ,						snake_case__     ) else self.tokenizer
							)
							lowerCAmelCase__        =   self.tokenizer.generator if isinstance(self.tokenizer  ,						snake_case__     ) else self.tokenizer

							lowerCAmelCase__        =   encode_line(snake_case__  ,						snake_case__  ,						self.max_source_length  ,						"""right"""     )
							lowerCAmelCase__        =   encode_line(snake_case__  ,						snake_case__  ,						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 						_SCREAMING_SNAKE_CASE						(	snake_case__						:     Union[str, Any]     ):
							return [len(snake_case__     ) for x in Path(snake_case__     ).open().readlines()]




					def 						_SCREAMING_SNAKE_CASE						(	self						:     int  ,						snake_case__						:     Optional[Any]     ):
							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  ,						snake_case__     )
							    else self.tokenizer.pad_token_id
							)
							lowerCAmelCase__        =   (
							    self.tokenizer.question_encoder.pad_token_id
							    if isinstance(self.tokenizer  ,						snake_case__     )
							    else self.tokenizer.pad_token_id
							)
							lowerCAmelCase__        =   trim_batch(snake_case__  ,						snake_case__     )
							lowerCAmelCase__   ,					lowerCAmelCase__        =   trim_batch(snake_case__  ,						snake_case__  ,						attention_mask=snake_case__     )
							lowerCAmelCase__        =   {
							    """input_ids""": source_ids,
							    """attention_mask""": source_mask,
							    """decoder_input_ids""": y,
							}
							return batch
__lowerCAmelCase    :							Optional[int]													=						getLogger(__name__)







def 							_UpperCAmelCase		(       lowerCamelCase__     ):





		"""simple docstring"""




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







def 							_UpperCAmelCase		(       lowerCamelCase__     ):





		"""simple docstring"""




		lowerCAmelCase__        =   get_git_info()
		save_json(lowerCamelCase__			,   os.path.join(lowerCamelCase__			,   """git_log.json"""     )     )







def 							_UpperCAmelCase		(       lowerCamelCase__			,   lowerCamelCase__			,   lowerCamelCase__=4			,   **lowerCamelCase__     ):





		"""simple docstring"""




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







def 							_UpperCAmelCase		(       lowerCamelCase__     ):





		"""simple docstring"""




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







def 							_UpperCAmelCase		(       ):





		"""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 							_UpperCAmelCase		(       lowerCamelCase__			,   lowerCamelCase__     ):





		"""simple docstring"""




		return list(map(lowerCamelCase__			,   lowerCamelCase__     )     )







def 							_UpperCAmelCase		(       lowerCamelCase__			,   lowerCamelCase__     ):





		"""simple docstring"""




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







def 							_UpperCAmelCase		(       lowerCamelCase__     ):





		"""simple docstring"""




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

		def white_space_fix(lowerCamelCase__     ):
				return " ".join(text.split()     )

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

		def lower(lowerCamelCase__     ):
				return text.lower()

		return white_space_fix(remove_articles(remove_punc(lower(lowerCamelCase__     )     )     )     )







def 							_UpperCAmelCase		(       lowerCamelCase__			,   lowerCamelCase__     ):





		"""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 							_UpperCAmelCase		(       lowerCamelCase__			,   lowerCamelCase__     ):





		"""simple docstring"""




		return normalize_answer(lowerCamelCase__     ) == normalize_answer(lowerCamelCase__     )







def 							_UpperCAmelCase		(       lowerCamelCase__			,   lowerCamelCase__     ):





		"""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 							_UpperCAmelCase		(       lowerCamelCase__     ):





		"""simple docstring"""




		return model_prefix.startswith("""rag"""     )







def 							_UpperCAmelCase		(       lowerCamelCase__			,   lowerCamelCase__			,   lowerCamelCase__     ):





		"""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"""




# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
import torch

from ..models.clipseg import CLIPSegForImageSegmentation
from ..utils import is_vision_available, requires_backends
from .base import PipelineTool


if is_vision_available():
			from PIL import Image

class       a_      (     __UpperCamelCase			):
					UpperCamelCase_ :	List[str]			=    (
					    "This is a tool that creates a segmentation mask of an image according to a label. It cannot create an image."
					    "It takes two arguments named `image` which should be the original image, and `label` which should be a text "
					    "describing the elements what should be identified in the segmentation mask. The tool returns the mask."
					)
					UpperCamelCase_ :	str			=    "CIDAS/clipseg-rd64-refined"
					UpperCamelCase_ :	Any			=    "image_segmenter"
					UpperCamelCase_ :	Optional[Any]			=    CLIPSegForImageSegmentation

					UpperCamelCase_ :	List[str]			=    ["image", "text"]
					UpperCamelCase_ :	int			=    ["image"]




					def __init__(	self						:     Tuple  ,						*snake_case__						:     str  ,						**snake_case__						:     Optional[Any]     ):
							requires_backends(self  ,						["""vision"""]     )
							super().__init__(*snake_case__  ,						**snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[Any]  ,						snake_case__						:     "Image"  ,						snake_case__						:     str     ):
							return self.pre_processor(text=[label]  ,						images=[image]  ,						padding=snake_case__  ,						return_tensors="""pt"""     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Dict  ,						snake_case__						:     Tuple     ):
							with torch.no_grad():
									lowerCAmelCase__        =   self.model(**snake_case__     ).logits
							return logits




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Dict  ,						snake_case__						:     List[Any]     ):
							lowerCAmelCase__        =   outputs.cpu().detach().numpy()
							lowerCAmelCase__        =   0
							lowerCAmelCase__        =   1
							return Image.fromarray((array * 255).astype(np.uinta     )     )




