




"""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 						_snake_case     (					_snake_case							:					Tuple				,  _snake_case							:					Optional[Any]				,  _snake_case							:					Dict				,  _snake_case							:					Tuple				,  _snake_case							:					Union[str, Any]=True				,  _snake_case							:					Optional[int]="pt"							)    ->					Optional[Any]:






      '''simple docstring'''


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






def 						_snake_case     (					_snake_case							:					List[str]				,  _snake_case							:					str				,  _snake_case							:					Optional[int]=None				,  )    ->					Optional[Any]:






      '''simple docstring'''


      _A					   =   input_ids.ne(lowercase_							).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_       (     __lowerCAmelCase							):



 '''simple docstring'''

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

 def __len__( self       :       Union[str, Any]		):
       return len(self.src_lens		)

 def __getitem__( self       :       int    ,							_UpperCAmelCase       :       Union[str, Any]		):
       _A					   =   index + 1  # linecache starts at 1
       _A					   =   self.prefix + linecache.getline(str(self.src_file		)    ,							A__		).rstrip('\n'		)
       _A					   =   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
       _A					   =   (
           self.tokenizer.question_encoder if isinstance(self.tokenizer    ,							A__		) else self.tokenizer
       )
       _A					   =   self.tokenizer.generator if isinstance(self.tokenizer    ,							A__		) else self.tokenizer

       _A					   =   encode_line(A__    ,							A__    ,							self.max_source_length    ,							'right'		)
       _A					   =   encode_line(A__    ,							A__    ,							self.max_target_length    ,							'right'		)

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

 @staticmethod
 def 			lowerCAmelCase_							( _UpperCAmelCase       :       Optional[int]		):
       return [len(A__		) for x in Path(A__		).open().readlines()]



 def 			lowerCAmelCase_							( self       :       Tuple    ,							_UpperCAmelCase       :       List[Any]		):
       _A					   =   torch.stack([x['input_ids'] for x in batch]		)
       _A					   =   torch.stack([x['attention_mask'] for x in batch]		)
       _A					   =   torch.stack([x['decoder_input_ids'] for x in batch]		)
       _A					   =   (
           self.tokenizer.generator.pad_token_id
           if isinstance(self.tokenizer    ,							A__		)
           else self.tokenizer.pad_token_id
       )
       _A					   =   (
           self.tokenizer.question_encoder.pad_token_id
           if isinstance(self.tokenizer    ,							A__		)
           else self.tokenizer.pad_token_id
       )
       _A					   =   trim_batch(A__    ,							A__		)
       _A					   =   trim_batch(A__    ,							A__    ,							attention_mask=A__		)
       _A					   =   {
           """input_ids""": source_ids,
           """attention_mask""": source_mask,
           """decoder_input_ids""": y,
       }
       return batch



a					=			getLogger(__name__)






def 						_snake_case     (					_snake_case							:					List[List]							)    ->					Optional[int]:






      '''simple docstring'''


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






def 						_snake_case     (					_snake_case							:					str							)    ->					None:






      '''simple docstring'''


      _A					   =   get_git_info()
      save_json(lowercase_				,  os.path.join(lowercase_				,  'git_log.json'							)							)






def 						_snake_case     (					_snake_case							:					Optional[int]				,  _snake_case							:					Any				,  _snake_case							:					Any=4				,  **_snake_case							:					List[Any]							)    ->					int:






      '''simple docstring'''


      with open(lowercase_				,  'w'							) as f:
            json.dump(lowercase_				,  lowercase_				,  indent=lowercase_				,  **lowercase_							)






def 						_snake_case     (					_snake_case							:					Tuple							)    ->					List[str]:






      '''simple docstring'''


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






def 						_snake_case     (					)    ->					Tuple:






      '''simple docstring'''


      _A					   =   git.Repo(search_parent_directories=lowercase_							)
      _A					   =   {
          """repo_id""": str(lowercase_							),
          """repo_sha""": str(repo.head.object.hexsha							),
          """repo_branch""": str(repo.active_branch							),
          """hostname""": str(socket.gethostname()							),
      }
      return repo_infos






def 						_snake_case     (					_snake_case							:					Callable				,  _snake_case							:					Iterable							)    ->					List:






      '''simple docstring'''


      return list(map(lowercase_				,  lowercase_							)							)






def 						_snake_case     (					_snake_case							:					List[Any]				,  _snake_case							:					Union[str, Any]							)    ->					Dict:






      '''simple docstring'''


      with open(lowercase_				,  'wb'							) as f:
            return pickle.dump(lowercase_				,  lowercase_							)






def 						_snake_case     (					_snake_case							:					Dict							)    ->					Dict:






      '''simple docstring'''


      def remove_articles(_snake_case							:					Dict							):
            return re.sub(R'\b(a|an|the)\b'				,  ' '				,  lowercase_							)

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

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

      def lower(_snake_case							:					Optional[int]							):
            return text.lower()

      return white_space_fix(remove_articles(remove_punc(lower(lowercase_							)							)							)							)






def 						_snake_case     (					_snake_case							:					str				,  _snake_case							:					List[Any]							)    ->					Any:






      '''simple docstring'''


      _A					   =   normalize_answer(lowercase_							).split()
      _A					   =   normalize_answer(lowercase_							).split()
      _A					   =   Counter(lowercase_							) & Counter(lowercase_							)
      _A					   =   sum(common.values()							)
      if num_same == 0:
            return 0
      _A					   =   1.0 * num_same / len(lowercase_							)
      _A					   =   1.0 * num_same / len(lowercase_							)
      _A					   =   (2 * precision * recall) / (precision + recall)
      return fa






def 						_snake_case     (					_snake_case							:					Optional[int]				,  _snake_case							:					List[Any]							)    ->					Optional[int]:






      '''simple docstring'''


      return normalize_answer(lowercase_							) == normalize_answer(lowercase_							)






def 						_snake_case     (					_snake_case							:					List[str]				,  _snake_case							:					List[str]							)    ->					Dict:






      '''simple docstring'''


      assert len(lowercase_							) == len(lowercase_							)
      _A					   =   0
      for hypo, pred in zip(lowercase_				,  lowercase_							):
            em += exact_match_score(lowercase_				,  lowercase_							)
      if len(lowercase_							) > 0:
            em /= len(lowercase_							)
      return {"em": em}






def 						_snake_case     (					_snake_case							:					Dict							)    ->					str:






      '''simple docstring'''


      return model_prefix.startswith('rag'							)







def 						_snake_case     (					_snake_case							:					Dict				,  _snake_case							:					Union[str, Any]				,  _snake_case							:					Dict							)    ->					Dict:






      '''simple docstring'''


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




import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel

from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()



class     _a						(unittest.TestCase				):


					'''simple docstring'''






					@property
					def   __A			( self			):
						torch.manual_seed(0			)
						A__							:    int        =		UNetaDModel(
						    block_out_channels=(32, 64)    ,       layers_per_block=2    ,       sample_size=32    ,       in_channels=3    ,       out_channels=3    ,       down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")    ,       up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")    ,       )
						return model






					@property
					def   __A			( self			):
						torch.manual_seed(0			)
						A__							:    Dict        =		VQModel(
						    block_out_channels=[32, 64]    ,       in_channels=3    ,       out_channels=3    ,       down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]    ,       up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]    ,       latent_channels=3    ,       )
						return model






					@property
					def   __A			( self			):
						torch.manual_seed(0			)
						A__							:    int        =		CLIPTextConfig(
						    bos_token_id=0    ,       eos_token_id=2    ,       hidden_size=32    ,       intermediate_size=37    ,       layer_norm_eps=1e-05    ,       num_attention_heads=4    ,       num_hidden_layers=5    ,       pad_token_id=1    ,       vocab_size=1000    ,       )
						return CLIPTextModel(A__			)






					def   __A			( self			):
						A__							:    int        =		self.dummy_uncond_unet
						A__							:    Optional[int]        =		DDIMScheduler()
						A__							:    List[Any]        =		self.dummy_vq_model

						A__							:    Optional[int]        =		LDMPipeline(unet=A__    ,       vqvae=A__    ,       scheduler=A__			)
						ldm.to(A__			)
						ldm.set_progress_bar_config(disable=A__			)

						A__							:    Tuple        =		torch.manual_seed(0			)
						A__							:    List[Any]        =		ldm(generator=A__    ,       num_inference_steps=2    ,       output_type="""numpy"""			).images

						A__							:    Optional[int]        =		torch.manual_seed(0			)
						A__							:    List[Any]        =		ldm(generator=A__    ,       num_inference_steps=2    ,       output_type="""numpy"""    ,       return_dict=A__			)[0]

						A__							:    str        =		image[0, -3:, -3:, -1]
						A__							:    Tuple        =		image_from_tuple[0, -3:, -3:, -1]

						assert image.shape == (1, 64, 64, 3)
						A__							:    Dict        =		np.array([0.8_5_1_2, 0.8_1_8, 0.6_4_1_1, 0.6_8_0_8, 0.4_4_6_5, 0.5_6_1_8, 0.4_6, 0.6_2_3_1, 0.5_1_7_2]			)
						A__							:    List[Any]        =		1e-2 if torch_device != """mps""" else 3e-2

						assert np.abs(image_slice.flatten() - expected_slice			).max() < tolerance
						assert np.abs(image_from_tuple_slice.flatten() - expected_slice			).max() < tolerance



@slow
@require_torch
class     _a						(unittest.TestCase				):


					'''simple docstring'''






					def   __A			( self			):
						A__							:    Optional[Any]        =		LDMPipeline.from_pretrained("""CompVis/ldm-celebahq-256"""			)
						ldm.to(A__			)
						ldm.set_progress_bar_config(disable=A__			)

						A__							:    Tuple        =		torch.manual_seed(0			)
						A__							:    List[Any]        =		ldm(generator=A__    ,       num_inference_steps=5    ,       output_type="""numpy"""			).images

						A__							:    int        =		image[0, -3:, -3:, -1]

						assert image.shape == (1, 256, 256, 3)
						A__							:    Any        =		np.array([0.4_3_9_9, 0.4_4_9_7_5, 0.4_6_8_2_5, 0.4_7_4, 0.4_3_5_9, 0.4_5_8_1, 0.4_5_0_9_5, 0.4_3_4_1, 0.4_4_4_7]			)
						A__							:    int        =		1e-2 if torch_device != """mps""" else 3e-2

						assert np.abs(image_slice.flatten() - expected_slice			).max() < tolerance



