






"""simple docstring"""
import unittest

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 PoolFormerImageProcessor



class   SCREAMING_SNAKE_CASE	(					unittest.TestCase    ):





	"""simple docstring"""




	def __init__(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_=7  ,  lowerCAmelCase_=3  ,  lowerCAmelCase_=30  ,  lowerCAmelCase_=400  ,  lowerCAmelCase_=True  ,  lowerCAmelCase_=None  ,  lowerCAmelCase_=0.9  ,  lowerCAmelCase_=None  ,  lowerCAmelCase_=True  ,  lowerCAmelCase_=[0.5, 0.5, 0.5]  ,  lowerCAmelCase_=[0.5, 0.5, 0.5]  ,  ):
		A_		:    List[Any]						     = size if size is not None else {"""shortest_edge""": 30}
		A_		:    Any						     = crop_size if crop_size is not None else {"""height""": 30, """width""": 30}
		A_		:    str						     = parent
		A_		:    List[Any]						     = batch_size
		A_		:    int						     = num_channels
		A_		:    List[Any]						     = min_resolution
		A_		:    Optional[int]						     = max_resolution
		A_		:    int						     = do_resize_and_center_crop
		A_		:    Tuple						     = size
		A_		:    Dict						     = crop_pct
		A_		:    Dict						     = crop_size
		A_		:    Optional[int]						     = do_normalize
		A_		:    int						     = image_mean
		A_		:    Any						     = image_std





	def  lowerCamelCase(self				):
		return {
		    "size": self.size,
		    "do_resize_and_center_crop": self.do_resize_and_center_crop,
		    "crop_pct": self.crop_pct,
		    "crop_size": self.crop_size,
		    "do_normalize": self.do_normalize,
		    "image_mean": self.image_mean,
		    "image_std": self.image_std,
		}






@require_torch
@require_vision
class   SCREAMING_SNAKE_CASE	(					_SCREAMING_SNAKE_CASE							,							unittest.TestCase    ):





	"""simple docstring"""




	_A							:					str 			=					PoolFormerImageProcessor if is_vision_available() else None





	def  lowerCamelCase(self				):
		A_		:    List[Any]						     = PoolFormerImageProcessingTester(self				)





	@property
	def  lowerCamelCase(self				):
		return self.image_processor_tester.prepare_image_processor_dict()





	def  lowerCamelCase(self				):
		A_		:    Any						     = self.image_processing_class(**self.image_processor_dict				)
		self.assertTrue(hasattr(lowerCAmelCase_  ,  """do_resize_and_center_crop"""				)				)
		self.assertTrue(hasattr(lowerCAmelCase_  ,  """size"""				)				)
		self.assertTrue(hasattr(lowerCAmelCase_  ,  """crop_pct"""				)				)
		self.assertTrue(hasattr(lowerCAmelCase_  ,  """do_normalize"""				)				)
		self.assertTrue(hasattr(lowerCAmelCase_  ,  """image_mean"""				)				)
		self.assertTrue(hasattr(lowerCAmelCase_  ,  """image_std"""				)				)





	def  lowerCamelCase(self				):
		A_		:    List[Any]						     = self.image_processing_class.from_dict(self.image_processor_dict				)
		self.assertEqual(image_processor.size  ,  {"""shortest_edge""": 30}				)
		self.assertEqual(image_processor.crop_size  ,  {"""height""": 30, """width""": 30}				)

		A_		:    int						     = self.image_processing_class.from_dict(self.image_processor_dict  ,  size=42  ,  crop_size=84				)
		self.assertEqual(image_processor.size  ,  {"""shortest_edge""": 42}				)
		self.assertEqual(image_processor.crop_size  ,  {"""height""": 84, """width""": 84}				)





	def  lowerCamelCase(self				):
		pass





	def  lowerCamelCase(self				):
		# Initialize image_processing
		A_		:    List[Any]						     = self.image_processing_class(**self.image_processor_dict				)
		# create random PIL images
		A_		:    List[Any]						     = prepare_image_inputs(self.image_processor_tester  ,  equal_resolution=lowerCAmelCase_				)
		for image in image_inputs:
			self.assertIsInstance(lowerCAmelCase_  ,  Image.Image				)

		# Test not batched input
		A_		:    Dict						     = image_processing(image_inputs[0]  ,  return_tensors="""pt"""				).pixel_values

		self.assertEqual(
		    encoded_images.shape  ,  (
		        1,
		        self.image_processor_tester.num_channels,
		        self.image_processor_tester.crop_size["""height"""],
		        self.image_processor_tester.crop_size["""width"""],
		    )  ,  )

		# Test batched
		A_		:    List[Any]						     = image_processing(lowerCAmelCase_  ,  return_tensors="""pt"""				).pixel_values
		self.assertEqual(
		    encoded_images.shape  ,  (
		        self.image_processor_tester.batch_size,
		        self.image_processor_tester.num_channels,
		        self.image_processor_tester.crop_size["""height"""],
		        self.image_processor_tester.crop_size["""width"""],
		    )  ,  )





	def  lowerCamelCase(self				):
		# Initialize image_processing
		A_		:    str						     = self.image_processing_class(**self.image_processor_dict				)
		# create random numpy tensors
		A_		:    Optional[int]						     = 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
		A_		:    Optional[int]						     = image_processing(image_inputs[0]  ,  return_tensors="""pt"""				).pixel_values
		self.assertEqual(
		    encoded_images.shape  ,  (
		        1,
		        self.image_processor_tester.num_channels,
		        self.image_processor_tester.crop_size["""height"""],
		        self.image_processor_tester.crop_size["""width"""],
		    )  ,  )

		# Test batched
		A_		:    str						     = image_processing(lowerCAmelCase_  ,  return_tensors="""pt"""				).pixel_values
		self.assertEqual(
		    encoded_images.shape  ,  (
		        self.image_processor_tester.batch_size,
		        self.image_processor_tester.num_channels,
		        self.image_processor_tester.crop_size["""height"""],
		        self.image_processor_tester.crop_size["""width"""],
		    )  ,  )





	def  lowerCamelCase(self				):
		# Initialize image_processing
		A_		:    Any						     = self.image_processing_class(**self.image_processor_dict				)
		# create random PyTorch tensors
		A_		:    Dict						     = 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
		A_		:    Optional[Any]						     = image_processing(image_inputs[0]  ,  return_tensors="""pt"""				).pixel_values
		self.assertEqual(
		    encoded_images.shape  ,  (
		        1,
		        self.image_processor_tester.num_channels,
		        self.image_processor_tester.crop_size["""height"""],
		        self.image_processor_tester.crop_size["""width"""],
		    )  ,  )

		# Test batched
		A_		:    Optional[Any]						     = image_processing(lowerCAmelCase_  ,  return_tensors="""pt"""				).pixel_values
		self.assertEqual(
		    encoded_images.shape  ,  (
		        self.image_processor_tester.batch_size,
		        self.image_processor_tester.num_channels,
		        self.image_processor_tester.crop_size["""height"""],
		        self.image_processor_tester.crop_size["""width"""],
		    )  ,  )









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






def       __UpperCamelCase						(		snake_case__ , snake_case__ , snake_case__=None , ):
	A_		:    int						     = input_ids.ne(snake_case__       ).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   SCREAMING_SNAKE_CASE	(					_SCREAMING_SNAKE_CASE    ):





	"""simple docstring"""




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





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





	def __getitem__(self  ,  lowerCAmelCase_				):
		A_		:    Optional[Any]						     = index + 1  # linecache starts at 1
		A_		:    int						     = self.prefix + linecache.getline(str(self.src_file				)  ,  lowerCAmelCase_				).rstrip("""\n"""				)
		A_		:    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
		A_		:    Optional[Any]						     = (
		    self.tokenizer.question_encoder if isinstance(self.tokenizer  ,  lowerCAmelCase_				) else self.tokenizer
		)
		A_		:    Optional[int]						     = self.tokenizer.generator if isinstance(self.tokenizer  ,  lowerCAmelCase_				) else self.tokenizer

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

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





	@staticmethod
	def  lowerCamelCase(lowerCAmelCase_				):
		return [len(lowerCAmelCase_				) for x in Path(lowerCAmelCase_				).open().readlines()]





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






_lowerCAmelCase       =				getLogger(__name__)






def       __UpperCamelCase						(		snake_case__       ):
	return list(itertools.chain.from_iterable(snake_case__       )       )






def       __UpperCamelCase						(		snake_case__       ):
	A_		:    List[str]						     = get_git_info()
	save_json(snake_case__ , os.path.join(snake_case__ , """git_log.json"""       )       )






def       __UpperCamelCase						(		snake_case__ , snake_case__ , snake_case__=4 , **snake_case__       ):
	with open(snake_case__ , """w"""       ) as f:
		json.dump(snake_case__ , snake_case__ , indent=snake_case__ , **snake_case__       )






def       __UpperCamelCase						(		snake_case__       ):
	with open(snake_case__       ) as f:
		return json.load(snake_case__       )






def       __UpperCamelCase						(		):
	A_		:    Optional[int]						     = git.Repo(search_parent_directories=snake_case__       )
	A_		:    Union[str, Any]						     = {
	    """repo_id""": str(snake_case__       ),
	    """repo_sha""": str(repo.head.object.hexsha       ),
	    """repo_branch""": str(repo.active_branch       ),
	    """hostname""": str(socket.gethostname()       ),
	}
	return repo_infos






def       __UpperCamelCase						(		snake_case__ , snake_case__       ):
	return list(map(snake_case__ , snake_case__       )       )






def       __UpperCamelCase						(		snake_case__ , snake_case__       ):
	with open(snake_case__ , """wb"""       ) as f:
		return pickle.dump(snake_case__ , snake_case__       )






def       __UpperCamelCase						(		snake_case__       ):

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

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

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

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

	return white_space_fix(remove_articles(remove_punc(lower(snake_case__       )       )       )       )






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






def       __UpperCamelCase						(		snake_case__ , snake_case__       ):
	return normalize_answer(snake_case__       ) == normalize_answer(snake_case__       )






def       __UpperCamelCase						(		snake_case__ , snake_case__       ):
	assert len(snake_case__       ) == len(snake_case__       )
	A_		:    Optional[Any]						     = 0
	for hypo, pred in zip(snake_case__ , snake_case__       ):
		em += exact_match_score(snake_case__ , snake_case__       )
	if len(snake_case__       ) > 0:
		em /= len(snake_case__       )
	return {"em": em}






def       __UpperCamelCase						(		snake_case__       ):
	return model_prefix.startswith("""rag"""       )






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

