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"	):







    '''simple docstring'''
    _lowerCamelCase     :		int 						=						{"""add_prefix_space""": True} if isinstance(__A   , __A	) and not line.startswith(""" """	) else {}
    _lowerCamelCase     :		List[Any] 						=						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   , ):







    '''simple docstring'''
    _lowerCamelCase     :		List[str] 						=						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 	__snake_case	( __lowerCAmelCase		):





      '''simple docstring'''






      def __init__(					self     :				List[Any]    ,  _UpperCamelCase     :				Optional[int]    ,  _UpperCamelCase     :				List[str]    ,  _UpperCamelCase     :				int    ,  _UpperCamelCase     :				Optional[int]    ,  _UpperCamelCase     :				Any="train"    ,  _UpperCamelCase     :				Union[str, Any]=None    ,  _UpperCamelCase     :				int=None    ,  _UpperCamelCase     :				Dict=None    ,  _UpperCamelCase     :				Any=""    ,  )				->str:


          """simple docstring"""


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



      def __len__(					self     :				List[str])				->Union[str, Any]:


          """simple docstring"""


          return len(self.src_lens)



      def __getitem__(					self     :				Optional[int]    ,  _UpperCamelCase     :				str)				->Dict[str, torch.Tensor]:


          """simple docstring"""


          _lowerCamelCase     :		Tuple 						=						index + 1  # linecache starts at 1
          _lowerCamelCase     :		Any 						=						self.prefix + linecache.getline(str(self.src_file)    ,  _UpperCamelCase).rstrip("""\n""")
          _lowerCamelCase     :		int 						=						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     :		Any 						=						(
              self.tokenizer.question_encoder if isinstance(self.tokenizer    ,  _UpperCamelCase) else self.tokenizer
          )
          _lowerCamelCase     :		Tuple 						=						self.tokenizer.generator if isinstance(self.tokenizer    ,  _UpperCamelCase) else self.tokenizer

          _lowerCamelCase     :		Union[str, Any] 						=						encode_line(_UpperCamelCase    ,  _UpperCamelCase    ,  self.max_source_length    ,  """right""")
          _lowerCamelCase     :		Optional[int] 						=						encode_line(_UpperCamelCase    ,  _UpperCamelCase    ,  self.max_target_length    ,  """right""")

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



      @staticmethod
      def 	_SCREAMING_SNAKE_CASE    (					_UpperCamelCase     :				int)				->Optional[Any]:


          """simple docstring"""


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




      def 	_SCREAMING_SNAKE_CASE    (					self     :				int    ,  _UpperCamelCase     :				int)				->Dict[str, torch.Tensor]:


          """simple docstring"""


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





lowerCAmelCase			:							Dict     =getLogger(__name__)







def 						A__  (					__A	):







    '''simple docstring'''
    return list(itertools.chain.from_iterable(__A	)	)







def 						A__  (					__A	):







    '''simple docstring'''
    _lowerCamelCase     :		str 						=						get_git_info()
    save_json(__A   , os.path.join(__A   , """git_log.json"""	)	)







def 						A__  (					__A   , __A   , __A=4   , **__A	):







    '''simple docstring'''
    with open(__A   , """w"""	) as f:
        json.dump(__A   , __A   , indent=__A   , **__A	)







def 						A__  (					__A	):







    '''simple docstring'''
    with open(__A	) as f:
        return json.load(__A	)







def 						A__  (					):







    '''simple docstring'''
    _lowerCamelCase     :		Tuple 						=						git.Repo(search_parent_directories=__A	)
    _lowerCamelCase     :		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	):







    '''simple docstring'''
    return list(map(__A   , __A	)	)







def 						A__  (					__A   , __A	):







    '''simple docstring'''
    with open(__A   , """wb"""	) as f:
        return pickle.dump(__A   , __A	)







def 						A__  (					__A	):







    '''simple docstring'''

    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	):
        _lowerCamelCase     :		str 						=						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	):







    '''simple docstring'''
    _lowerCamelCase     :		int 						=						normalize_answer(__A	).split()
    _lowerCamelCase     :		Optional[int] 						=						normalize_answer(__A	).split()
    _lowerCamelCase     :		int 						=						Counter(__A	) & Counter(__A	)
    _lowerCamelCase     :		Optional[Any] 						=						sum(common.values()	)
    if num_same == 0:
        return 0
    _lowerCamelCase     :		Union[str, Any] 						=						1.0 * num_same / len(__A	)
    _lowerCamelCase     :		Tuple 						=						1.0 * num_same / len(__A	)
    _lowerCamelCase     :		Tuple 						=						(2 * precision * recall) / (precision + recall)
    return fa







def 						A__  (					__A   , __A	):







    '''simple docstring'''
    return normalize_answer(__A	) == normalize_answer(__A	)







def 						A__  (					__A   , __A	):







    '''simple docstring'''
    assert len(__A	) == len(__A	)
    _lowerCamelCase     :		Tuple 						=						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	):







    '''simple docstring'''
    return model_prefix.startswith("""rag"""	)







def 						A__  (					__A   , __A   , __A	):







    '''simple docstring'''
    _lowerCamelCase     :		List[str] 						=						{p: p for p in extra_params}
    # T5 models don't have `dropout` param, they have `dropout_rate` instead
    _lowerCamelCase     :		int 						=						"""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
            _lowerCamelCase     :		str 						=						p if hasattr(__A   , __A	) else equivalent_param[p]
            setattr(__A   , __A   , getattr(__A   , __A	)	)
            delattr(__A   , __A	)
    return hparams, config





import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_torch_available, is_vision_available


if is_torch_available():
    import torch


if is_vision_available():
    from PIL import Image

    from transformers import MgpstrProcessor, ViTImageProcessor




@require_torch
@require_vision
class 	__snake_case	( unittest.TestCase		):





      '''simple docstring'''






      _snake_case    		=       ViTImageProcessor if is_vision_available() else None



      @property
      def 	_SCREAMING_SNAKE_CASE    (					self     :				Dict)				->Dict:


          """simple docstring"""


          return self.image_processor_tester.prepare_image_processor_dict()



      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[str])				->List[Any]:


          """simple docstring"""


          _lowerCamelCase     :		Union[str, Any] 						=						(3, 32, 128)
          _lowerCamelCase     :		str 						=						tempfile.mkdtemp()

          # fmt: off
          _lowerCamelCase     :		Dict 						=						["""[GO]""", """[s]""", """0""", """1""", """2""", """3""", """4""", """5""", """6""", """7""", """8""", """9""", """a""", """b""", """c""", """d""", """e""", """f""", """g""", """h""", """i""", """j""", """k""", """l""", """m""", """n""", """o""", """p""", """q""", """r""", """s""", """t""", """u""", """v""", """w""", """x""", """y""", """z"""]
          # fmt: on
          _lowerCamelCase     :		str 						=						dict(zip(_UpperCamelCase    ,  range(len(_UpperCamelCase))))

          _lowerCamelCase     :		int 						=						os.path.join(self.tmpdirname    ,  VOCAB_FILES_NAMES["""vocab_file"""])
          with open(self.vocab_file    ,  """w"""    ,  encoding="""utf-8""") as fp:
              fp.write(json.dumps(_UpperCamelCase) + """\n""")

          _lowerCamelCase     :		Any 						=						{
              """do_normalize""": False,
              """do_resize""": True,
              """image_processor_type""": """ViTImageProcessor""",
              """resample""": 3,
              """size""": {"""height""": 32, """width""": 128},
          }
          _lowerCamelCase     :		Union[str, Any] 						=						os.path.join(self.tmpdirname    ,  _UpperCamelCase)
          with open(self.image_processor_file    ,  """w"""    ,  encoding="""utf-8""") as fp:
              json.dump(_UpperCamelCase    ,  _UpperCamelCase)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[Any]    ,  **_UpperCamelCase     :				Any)				->Tuple:


          """simple docstring"""


          return MgpstrTokenizer.from_pretrained(self.tmpdirname    ,  **_UpperCamelCase)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Dict    ,  **_UpperCamelCase     :				Optional[Any])				->List[Any]:


          """simple docstring"""


          return ViTImageProcessor.from_pretrained(self.tmpdirname    ,  **_UpperCamelCase)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[Any])				->Optional[Any]:


          """simple docstring"""


          shutil.rmtree(self.tmpdirname)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[int])				->Any:


          """simple docstring"""


          _lowerCamelCase     :		Tuple 						=						np.random.randint(255    ,  size=(3, 30, 400)    ,  dtype=np.uinta)

          _lowerCamelCase     :		int 						=						Image.fromarray(np.moveaxis(_UpperCamelCase    ,  0    ,  -1))

          return image_input



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Any)				->str:


          """simple docstring"""


          _lowerCamelCase     :		List[str] 						=						self.get_tokenizer()
          _lowerCamelCase     :		Tuple 						=						self.get_image_processor()

          _lowerCamelCase     :		Union[str, Any] 						=						MgpstrProcessor(tokenizer=_UpperCamelCase    ,  image_processor=_UpperCamelCase)
          processor.save_pretrained(self.tmpdirname)
          _lowerCamelCase     :		int 						=						MgpstrProcessor.from_pretrained(self.tmpdirname    ,  use_fast=_UpperCamelCase)

          self.assertEqual(processor.char_tokenizer.get_vocab()    ,  tokenizer.get_vocab())
          self.assertIsInstance(processor.char_tokenizer    ,  _UpperCamelCase)

          self.assertEqual(processor.image_processor.to_json_string()    ,  image_processor.to_json_string())
          self.assertIsInstance(processor.image_processor    ,  _UpperCamelCase)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Dict)				->Dict:


          """simple docstring"""


          _lowerCamelCase     :		Dict 						=						self.get_tokenizer()
          _lowerCamelCase     :		Optional[Any] 						=						self.get_image_processor()

          _lowerCamelCase     :		Any 						=						MgpstrProcessor(tokenizer=_UpperCamelCase    ,  image_processor=_UpperCamelCase)
          processor.save_pretrained(self.tmpdirname)

          _lowerCamelCase     :		Tuple 						=						self.get_tokenizer(bos_token="""(BOS)"""    ,  eos_token="""(EOS)""")
          _lowerCamelCase     :		Union[str, Any] 						=						self.get_image_processor(do_normalize=_UpperCamelCase    ,  padding_value=1.0)

          _lowerCamelCase     :		Tuple 						=						MgpstrProcessor.from_pretrained(
              self.tmpdirname    ,  bos_token="""(BOS)"""    ,  eos_token="""(EOS)"""    ,  do_normalize=_UpperCamelCase    ,  padding_value=1.0)

          self.assertEqual(processor.char_tokenizer.get_vocab()    ,  tokenizer_add_kwargs.get_vocab())
          self.assertIsInstance(processor.char_tokenizer    ,  _UpperCamelCase)

          self.assertEqual(processor.image_processor.to_json_string()    ,  image_processor_add_kwargs.to_json_string())
          self.assertIsInstance(processor.image_processor    ,  _UpperCamelCase)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Any)				->int:


          """simple docstring"""


          _lowerCamelCase     :		int 						=						self.get_image_processor()
          _lowerCamelCase     :		int 						=						self.get_tokenizer()

          _lowerCamelCase     :		List[str] 						=						MgpstrProcessor(tokenizer=_UpperCamelCase    ,  image_processor=_UpperCamelCase)

          _lowerCamelCase     :		List[str] 						=						self.prepare_image_inputs()

          _lowerCamelCase     :		Optional[int] 						=						image_processor(_UpperCamelCase    ,  return_tensors="""np""")
          _lowerCamelCase     :		int 						=						processor(images=_UpperCamelCase    ,  return_tensors="""np""")

          for key in input_image_proc.keys():
              self.assertAlmostEqual(input_image_proc[key].sum()    ,  input_processor[key].sum()    ,  delta=1E-2)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[Any])				->List[Any]:


          """simple docstring"""


          _lowerCamelCase     :		List[Any] 						=						self.get_image_processor()
          _lowerCamelCase     :		int 						=						self.get_tokenizer()

          _lowerCamelCase     :		Any 						=						MgpstrProcessor(tokenizer=_UpperCamelCase    ,  image_processor=_UpperCamelCase)

          _lowerCamelCase     :		Optional[int] 						=						"""test"""

          _lowerCamelCase     :		Union[str, Any] 						=						processor(text=_UpperCamelCase)

          _lowerCamelCase     :		Dict 						=						tokenizer(_UpperCamelCase)
          for key in encoded_tok.keys():
              self.assertListEqual(encoded_tok[key]    ,  encoded_processor[key])



      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[Any])				->Optional[Any]:


          """simple docstring"""


          _lowerCamelCase     :		Union[str, Any] 						=						self.get_image_processor()
          _lowerCamelCase     :		List[Any] 						=						self.get_tokenizer()

          _lowerCamelCase     :		Any 						=						MgpstrProcessor(tokenizer=_UpperCamelCase    ,  image_processor=_UpperCamelCase)

          _lowerCamelCase     :		Any 						=						"""test"""
          _lowerCamelCase     :		List[str] 						=						self.prepare_image_inputs()

          _lowerCamelCase     :		int 						=						processor(text=_UpperCamelCase    ,  images=_UpperCamelCase)

          self.assertListEqual(list(inputs.keys())    ,  ["""pixel_values""", """labels"""])

          # test if it raises when no input is passed
          with pytest.raises(_UpperCamelCase):
              processor()



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Any)				->str:


          """simple docstring"""


          _lowerCamelCase     :		Union[str, Any] 						=						self.get_image_processor()
          _lowerCamelCase     :		List[str] 						=						self.get_tokenizer()

          _lowerCamelCase     :		Dict 						=						MgpstrProcessor(tokenizer=_UpperCamelCase    ,  image_processor=_UpperCamelCase)

          _lowerCamelCase     :		int 						=						[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]

          _lowerCamelCase     :		Any 						=						processor.char_decode(_UpperCamelCase)
          _lowerCamelCase     :		Tuple 						=						tokenizer.batch_decode(_UpperCamelCase)
          _lowerCamelCase     :		List[str] 						=						[seq.replace(""" """    ,  """""") for seq in decoded_tok]

          self.assertListEqual(_UpperCamelCase    ,  _UpperCamelCase)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[int])				->str:


          """simple docstring"""


          _lowerCamelCase     :		Dict 						=						self.get_image_processor()
          _lowerCamelCase     :		str 						=						self.get_tokenizer()

          _lowerCamelCase     :		List[Any] 						=						MgpstrProcessor(tokenizer=_UpperCamelCase    ,  image_processor=_UpperCamelCase)

          _lowerCamelCase     :		int 						=						None
          _lowerCamelCase     :		Union[str, Any] 						=						self.prepare_image_inputs()

          _lowerCamelCase     :		Union[str, Any] 						=						processor(text=_UpperCamelCase    ,  images=_UpperCamelCase)

          self.assertListEqual(list(inputs.keys())    ,  processor.model_input_names)




      def 	_SCREAMING_SNAKE_CASE    (					self     :				Union[str, Any])				->Union[str, Any]:


          """simple docstring"""


          _lowerCamelCase     :		List[str] 						=						self.get_image_processor()
          _lowerCamelCase     :		int 						=						self.get_tokenizer()

          _lowerCamelCase     :		Union[str, Any] 						=						MgpstrProcessor(tokenizer=_UpperCamelCase    ,  image_processor=_UpperCamelCase)

          _lowerCamelCase     :		Any 						=						torch.randn(1    ,  27    ,  38)
          _lowerCamelCase     :		List[Any] 						=						torch.randn(1    ,  27    ,  5_0257)
          _lowerCamelCase     :		List[str] 						=						torch.randn(1    ,  27    ,  3_0522)

          _lowerCamelCase     :		int 						=						processor.batch_decode([char_input, bpe_input, wp_input])

          self.assertListEqual(list(results.keys())    ,  ["""generated_text""", """scores""", """char_preds""", """bpe_preds""", """wp_preds"""])




