


import unittest
from typing import Dict, List, Optional, Union

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
				import torch

if is_vision_available():
				from PIL import Image

				from transformers import BridgeTowerImageProcessor







class 	__lowercase    (unittest.TestCase  ):





							"""simple docstring"""







							def __init__(      self			,						lowerCAmelCase__			,						lowerCAmelCase__ = True			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = 3_2			,						lowerCAmelCase__ = True			,						lowerCAmelCase__ = 1 / 2_5_5			,						lowerCAmelCase__ = True			,						lowerCAmelCase__ = True			,						lowerCAmelCase__ = [0.48_145_466, 0.4_578_275, 0.40_821_073]			,						lowerCAmelCase__ = [0.26_862_954, 0.26_130_258, 0.27_577_711]			,						lowerCAmelCase__ = True			,						lowerCAmelCase__=7			,						lowerCAmelCase__=3_0			,						lowerCAmelCase__=4_0_0			,						lowerCAmelCase__=3			,						):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     List[Any]					=					parent
										SCREAMING_SNAKE_CASE_		:     int					=					do_resize
										SCREAMING_SNAKE_CASE_		:     Dict					=					size if size is not None else {'shortest_edge': 2_8_8}
										SCREAMING_SNAKE_CASE_		:     Tuple					=					size_divisor
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					do_rescale
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					rescale_factor
										SCREAMING_SNAKE_CASE_		:     Dict					=					do_normalize
										SCREAMING_SNAKE_CASE_		:     Any					=					do_center_crop
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					image_mean
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					image_std
										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					do_pad
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					batch_size
										SCREAMING_SNAKE_CASE_		:     str					=					num_channels
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					min_resolution
										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					max_resolution






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										return {
										    "image_mean": self.image_mean,
										    "image_std": self.image_std,
										    "do_normalize": self.do_normalize,
										    "do_resize": self.do_resize,
										    "size": self.size,
										    "size_divisor": self.size_divisor,
										}






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__			,						lowerCAmelCase__=False       ):





										"""simple docstring"""




										if not batched:
													SCREAMING_SNAKE_CASE_		:     str					=					self.size['shortest_edge']
													SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					image_inputs[0]
													if isinstance(lowerCAmelCase__			,						Image.Image       ):
																SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		:     str					=					image.size
													else:
																SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		:     List[str]					=					image.shape[1], image.shape[2]
													SCREAMING_SNAKE_CASE_		:     Dict					=					size / min(lowerCAmelCase__			,						lowerCAmelCase__       )
													if h < w:
																SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		:     Tuple					=					size, scale * w
													else:
																SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		:     List[Any]					=					scale * h, size

													SCREAMING_SNAKE_CASE_		:     Any					=					int((1_3_3_3 / 8_0_0) * size       )
													if max(lowerCAmelCase__			,						lowerCAmelCase__       ) > max_size:
																SCREAMING_SNAKE_CASE_		:     List[Any]					=					max_size / max(lowerCAmelCase__			,						lowerCAmelCase__       )
																SCREAMING_SNAKE_CASE_		:     int					=					newh * scale
																SCREAMING_SNAKE_CASE_		:     List[Any]					=					neww * scale

													SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		:     List[Any]					=					int(newh + 0.5       ), int(neww + 0.5       )
													SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					(
													    newh // self.size_divisor * self.size_divisor,
													    neww // self.size_divisor * self.size_divisor,
													)

										else:
													SCREAMING_SNAKE_CASE_		:     Dict					=					[]
													for image in image_inputs:
																SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					self.get_expected_values([image]       )
																expected_values.append((expected_height, expected_width)       )
													SCREAMING_SNAKE_CASE_		:     List[str]					=					max(lowerCAmelCase__			,						key=lambda lowerCAmelCase__       : item[0]       )[0]
													SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					max(lowerCAmelCase__			,						key=lambda lowerCAmelCase__       : item[1]       )[1]

										return expected_height, expected_width







@require_torch
@require_vision
class 	__lowercase    (__SCREAMING_SNAKE_CASE	,       unittest.TestCase  ):





							"""simple docstring"""







							_UpperCAmelCase      =						BridgeTowerImageProcessor if is_vision_available() else None






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     str					=					BridgeTowerImageProcessingTester(self       )






							@property
							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										return self.image_processor_tester.prepare_image_processor_dict()






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					self.image_processing_class(**self.image_processor_dict       )
										self.assertTrue(hasattr(lowerCAmelCase__			,						'image_mean'       )       )
										self.assertTrue(hasattr(lowerCAmelCase__			,						'image_std'       )       )
										self.assertTrue(hasattr(lowerCAmelCase__			,						'do_normalize'       )       )
										self.assertTrue(hasattr(lowerCAmelCase__			,						'do_resize'       )       )
										self.assertTrue(hasattr(lowerCAmelCase__			,						'size'       )       )
										self.assertTrue(hasattr(lowerCAmelCase__			,						'size_divisor'       )       )






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										pass






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     List[Any]					=					self.image_processing_class(**self.image_processor_dict       )
										# create random PIL images
										SCREAMING_SNAKE_CASE_		:     List[str]					=					prepare_image_inputs(self.image_processor_tester			,						equal_resolution=lowerCAmelCase__       )
										for image in image_inputs:
													self.assertIsInstance(lowerCAmelCase__			,						Image.Image       )

										# Test not batched input
										SCREAMING_SNAKE_CASE_		:     Any					=					image_processing(image_inputs[0]			,						return_tensors='pt'       ).pixel_values

										SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		:     List[Any]					=					self.image_processor_tester.get_expected_values(lowerCAmelCase__       )
										self.assertEqual(
										    encoded_images.shape			,						(1, self.image_processor_tester.num_channels, expected_height, expected_width)			,						)

										# Test batched
										SCREAMING_SNAKE_CASE_		:     List[str]					=					image_processing(lowerCAmelCase__			,						return_tensors='pt'       ).pixel_values

										SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		:     int					=					self.image_processor_tester.get_expected_values(lowerCAmelCase__			,						batched=lowerCAmelCase__       )
										self.assertEqual(
										    encoded_images.shape			,						(
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        expected_height,
										        expected_width,
										    )			,						)






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     List[str]					=					self.image_processing_class(**self.image_processor_dict       )
										# create random numpy tensors
										SCREAMING_SNAKE_CASE_		:     Tuple					=					prepare_image_inputs(self.image_processor_tester			,						equal_resolution=lowerCAmelCase__			,						numpify=lowerCAmelCase__       )
										for image in image_inputs:
													self.assertIsInstance(lowerCAmelCase__			,						np.ndarray       )

										# Test not batched input
										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					image_processing(image_inputs[0]			,						return_tensors='pt'       ).pixel_values

										SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		:     List[str]					=					self.image_processor_tester.get_expected_values(lowerCAmelCase__       )
										self.assertEqual(
										    encoded_images.shape			,						(1, self.image_processor_tester.num_channels, expected_height, expected_width)			,						)

										# Test batched
										SCREAMING_SNAKE_CASE_		:     List[str]					=					image_processing(lowerCAmelCase__			,						return_tensors='pt'       ).pixel_values

										SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		:     Optional[int]					=					self.image_processor_tester.get_expected_values(lowerCAmelCase__			,						batched=lowerCAmelCase__       )
										self.assertEqual(
										    encoded_images.shape			,						(
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        expected_height,
										        expected_width,
										    )			,						)






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Tuple					=					self.image_processing_class(**self.image_processor_dict       )
										# create random PyTorch tensors
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					prepare_image_inputs(self.image_processor_tester			,						equal_resolution=lowerCAmelCase__			,						torchify=lowerCAmelCase__       )
										for image in image_inputs:
													self.assertIsInstance(lowerCAmelCase__			,						torch.Tensor       )

										# Test not batched input
										SCREAMING_SNAKE_CASE_		:     int					=					image_processing(image_inputs[0]			,						return_tensors='pt'       ).pixel_values

										SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		:     int					=					self.image_processor_tester.get_expected_values(lowerCAmelCase__       )
										self.assertEqual(
										    encoded_images.shape			,						(1, self.image_processor_tester.num_channels, expected_height, expected_width)			,						)

										# Test batched
										SCREAMING_SNAKE_CASE_		:     int					=					image_processing(lowerCAmelCase__			,						return_tensors='pt'       ).pixel_values

										SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		:     List[Any]					=					self.image_processor_tester.get_expected_values(lowerCAmelCase__			,						batched=lowerCAmelCase__       )
										self.assertEqual(
										    encoded_images.shape			,						(
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        expected_height,
										        expected_width,
										    )			,						)









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 						a__			(				A__,      A__,      A__,      A__,      A__=True,      A__="pt"				):
			SCREAMING_SNAKE_CASE_		:     Tuple					=					{'add_prefix_space': True} if isinstance(A__,      A__				) and not line.startswith(' '				) else {}
			SCREAMING_SNAKE_CASE_		:     Dict					=					padding_side
			return tokenizer(
			    [line],      max_length=A__,      padding='max_length' if pad_to_max_length else None,      truncation=A__,      return_tensors=A__,      add_special_tokens=A__,      **A__,      )






def 						a__			(				A__,      A__,      A__=None,      ):
			SCREAMING_SNAKE_CASE_		:     int					=					input_ids.ne(A__				).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 	__lowercase    (__SCREAMING_SNAKE_CASE  ):





							"""simple docstring"""







							def __init__(      self			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__="train"			,						lowerCAmelCase__=None			,						lowerCAmelCase__=None			,						lowerCAmelCase__=None			,						lowerCAmelCase__=""			,						):





										"""simple docstring"""




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






							def __len__(      self       ):





										"""simple docstring"""




										return len(self.src_lens       )






							def __getitem__(      self			,						lowerCAmelCase__       ):





										"""simple docstring"""




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

										# Pad source and target to the right
										SCREAMING_SNAKE_CASE_		:     Any					=					(
										    self.tokenizer.question_encoder if isinstance(self.tokenizer			,						lowerCAmelCase__       ) else self.tokenizer
										)
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					self.tokenizer.generator if isinstance(self.tokenizer			,						lowerCAmelCase__       ) else self.tokenizer

										SCREAMING_SNAKE_CASE_		:     Dict					=					encode_line(lowerCAmelCase__			,						lowerCAmelCase__			,						self.max_source_length			,						'right'       )
										SCREAMING_SNAKE_CASE_		:     Tuple					=					encode_line(lowerCAmelCase__			,						lowerCAmelCase__			,						self.max_target_length			,						'right'       )

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






							@staticmethod
							def 					UpperCamelCase__     (      lowerCAmelCase__       ):





										"""simple docstring"""




										return [len(lowerCAmelCase__       ) for x in Path(lowerCAmelCase__       ).open().readlines()]






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__       ):





										"""simple docstring"""




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







lowerCAmelCase__			:      Tuple								=getLogger(__name__)






def 						a__			(				A__				):
			return list(itertools.chain.from_iterable(A__				)				)






def 						a__			(				A__				):
			SCREAMING_SNAKE_CASE_		:     Optional[int]					=					get_git_info()
			save_json(A__,      os.path.join(A__,      'git_log.json'				)				)






def 						a__			(				A__,      A__,      A__=4,      **A__				):
			with open(A__,      'w'				) as f:
						json.dump(A__,      A__,      indent=A__,      **A__				)






def 						a__			(				A__				):
			with open(A__				) as f:
						return json.load(A__				)






def 						a__			(				):
			SCREAMING_SNAKE_CASE_		:     List[str]					=					git.Repo(search_parent_directories=A__				)
			SCREAMING_SNAKE_CASE_		:     Any					=					{
			    'repo_id': str(A__				),
			    'repo_sha': str(repo.head.object.hexsha				),
			    'repo_branch': str(repo.active_branch				),
			    'hostname': str(socket.gethostname()				),
			}
			return repo_infos






def 						a__			(				A__,      A__				):
			return list(map(A__,      A__				)				)






def 						a__			(				A__,      A__				):
			with open(A__,      'wb'				) as f:
						return pickle.dump(A__,      A__				)






def 						a__			(				A__				):

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

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

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

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

			return white_space_fix(remove_articles(remove_punc(lower(A__				)				)				)				)






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






def 						a__			(				A__,      A__				):
			return normalize_answer(A__				) == normalize_answer(A__				)






def 						a__			(				A__,      A__				):
			assert len(A__				) == len(A__				)
			SCREAMING_SNAKE_CASE_		:     Any					=					0
			for hypo, pred in zip(A__,      A__				):
						em += exact_match_score(A__,      A__				)
			if len(A__				) > 0:
						em /= len(A__				)
			return {"em": em}






def 						a__			(				A__				):
			return model_prefix.startswith('rag'				)






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





