




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 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_					,     lowercase_					,     lowercase_					,     lowercase_=True					,     lowercase_="pt"      )       ->	Any:

    """simple docstring"""


    __UpperCamelCase  		=						{"add_prefix_space": True} if isinstance(__snake_case					,     __snake_case      ) and not line.startswith(''' '''      ) else {}
    __UpperCamelCase  		=						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 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_					,     lowercase_=None					,     )       ->	Any:

    """simple docstring"""


    __UpperCamelCase  		=						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  _lowerCamelCase						(			_A   ):

      """simple docstring"""

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







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







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

          __UpperCamelCase  		=						encode_line(snake_case ,       snake_case ,       self.max_source_length ,       '''right'''				)
          __UpperCamelCase  		=						encode_line(snake_case ,       snake_case ,       self.max_target_length ,       '''right'''				)

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







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







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







a_   		=     getLogger(__name__)




def 					__SCREAMING_SNAKE_CASE   (  lowercase_      )       ->	Dict:

    """simple docstring"""


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




def 					__SCREAMING_SNAKE_CASE   (  lowercase_      )       ->	None:

    """simple docstring"""


    __UpperCamelCase  		=						get_git_info()
    save_json(__snake_case					,     os.path.join(__snake_case					,     '''git_log.json'''      )      )




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_					,     lowercase_=4					,     **lowercase_      )       ->	Tuple:

    """simple docstring"""


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




def 					__SCREAMING_SNAKE_CASE   (  lowercase_      )       ->	Any:

    """simple docstring"""


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




def 					__SCREAMING_SNAKE_CASE   (  )       ->	Optional[Any]:

    """simple docstring"""


    __UpperCamelCase  		=						git.Repo(search_parent_directories=__snake_case      )
    __UpperCamelCase  		=						{
        "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 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_      )       ->	List:

    """simple docstring"""


    return list(map(__snake_case					,     __snake_case      )      )




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_      )       ->	Tuple:

    """simple docstring"""


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




def 					__SCREAMING_SNAKE_CASE   (  lowercase_      )       ->	List[Any]:

    """simple docstring"""


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

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

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

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

    return white_space_fix(remove_articles(remove_punc(lower(__snake_case      )      )      )      )




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_      )       ->	int:

    """simple docstring"""


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




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_      )       ->	Tuple:

    """simple docstring"""


    return normalize_answer(__snake_case      ) == normalize_answer(__snake_case      )




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_      )       ->	Dict:

    """simple docstring"""


    assert len(__snake_case      ) == len(__snake_case      )
    __UpperCamelCase  		=						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 					__SCREAMING_SNAKE_CASE   (  lowercase_      )       ->	Dict:

    """simple docstring"""


    return model_prefix.startswith('''rag'''      )




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_					,     lowercase_      )       ->	List[Any]:

    """simple docstring"""


    __UpperCamelCase  		=						{p: p for p in extra_params}
    # T5 models don't have `dropout` param, they have `dropout_rate` instead
    __UpperCamelCase  		=						"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
            __UpperCamelCase  		=						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












import gc
import random
import unittest

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

from diffusers import (
    AutoencoderKL,
    DiffusionPipeline,
    EulerDiscreteScheduler,
    StableDiffusionXLImgaImgPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()

class  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE							,			_SCREAMING_SNAKE_CASE							,			unittest.TestCase   ):

      """simple docstring"""

      lowerCAmelCase__			: Optional[int]    =					StableDiffusionXLImgaImgPipeline
      lowerCAmelCase__			: List[str]    =					TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width"}
      lowerCAmelCase__			: int    =					PipelineTesterMixin.required_optional_params - {"latents"}
      lowerCAmelCase__			: Tuple    =					TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
      lowerCAmelCase__			: int    =					IMAGE_TO_IMAGE_IMAGE_PARAMS
      lowerCAmelCase__			: List[Any]    =					IMAGE_TO_IMAGE_IMAGE_PARAMS







      def       snake_case	(     self					: Union[str, Any]				):
          torch.manual_seed(0				)
          __UpperCamelCase  		=						UNetaDConditionModel(
              block_out_channels=(32, 64) ,       layers_per_block=2 ,       sample_size=32 ,       in_channels=4 ,       out_channels=4 ,       down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''') ,       up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''') ,       attention_head_dim=(2, 4) ,       use_linear_projection=snake_case ,       addition_embed_type='''text_time''' ,       addition_time_embed_dim=8 ,       transformer_layers_per_block=(1, 2) ,       projection_class_embeddings_input_dim=80 ,       cross_attention_dim=64 ,       )
          __UpperCamelCase  		=						EulerDiscreteScheduler(
              beta_start=0.0_00_85 ,       beta_end=0.0_12 ,       steps_offset=1 ,       beta_schedule='''scaled_linear''' ,       timestep_spacing='''leading''' ,       )
          torch.manual_seed(0				)
          __UpperCamelCase  		=						AutoencoderKL(
              block_out_channels=[32, 64] ,       in_channels=3 ,       out_channels=3 ,       down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''] ,       up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''] ,       latent_channels=4 ,       sample_size=128 ,       )
          torch.manual_seed(0				)
          __UpperCamelCase  		=						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 ,       hidden_act='''gelu''' ,       projection_dim=32 ,       )
          __UpperCamelCase  		=						CLIPTextModel(snake_case				)
          __UpperCamelCase  		=						CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' ,       local_files_only=snake_case				)

          __UpperCamelCase  		=						CLIPTextModelWithProjection(snake_case				)
          __UpperCamelCase  		=						CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' ,       local_files_only=snake_case				)

          __UpperCamelCase  		=						{
              '''unet''': unet,
              '''scheduler''': scheduler,
              '''vae''': vae,
              '''text_encoder''': text_encoder,
              '''tokenizer''': tokenizer,
              '''text_encoder_2''': text_encoder_a,
              '''tokenizer_2''': tokenizer_a,
              # "safety_checker": None,
              # "feature_extractor": None,
          }
          return components







      def       snake_case	(     self					: List[Any] ,       snake_case					: List[Any] ,       snake_case					: str=0				):
          __UpperCamelCase  		=						floats_tensor((1, 3, 32, 32) ,       rng=random.Random(snake_case				)				).to(snake_case				)
          __UpperCamelCase  		=						image / 2 + 0.5
          if str(snake_case				).startswith('''mps'''				):
              __UpperCamelCase  		=						torch.manual_seed(snake_case				)
          else:
              __UpperCamelCase  		=						torch.Generator(device=snake_case				).manual_seed(snake_case				)
          __UpperCamelCase  		=						{
              '''prompt''': '''A painting of a squirrel eating a burger''',
              '''image''': image,
              '''generator''': generator,
              '''num_inference_steps''': 2,
              '''guidance_scale''': 5.0,
              '''output_type''': '''numpy''',
              '''strength''': 0.75,
          }
          return inputs







      def       snake_case	(     self					: Tuple				):
          __UpperCamelCase  		=						'''cpu'''  # ensure determinism for the device-dependent torch.Generator
          __UpperCamelCase  		=						self.get_dummy_components()
          __UpperCamelCase  		=						StableDiffusionXLImgaImgPipeline(**snake_case				)
          __UpperCamelCase  		=						sd_pipe.to(snake_case				)
          sd_pipe.set_progress_bar_config(disable=snake_case				)

          __UpperCamelCase  		=						self.get_dummy_inputs(snake_case				)
          __UpperCamelCase  		=						sd_pipe(**snake_case				).images
          __UpperCamelCase  		=						image[0, -3:, -3:, -1]

          assert image.shape == (1, 32, 32, 3)

          __UpperCamelCase  		=						np.array([0.46_56, 0.48_40, 0.44_39, 0.66_98, 0.55_74, 0.45_24, 0.57_99, 0.59_43, 0.51_65]				)

          assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-2







      def       snake_case	(     self					: Optional[int]				):
          super().test_attention_slicing_forward_pass(expected_max_diff=3E-3				)







      def       snake_case	(     self					: Optional[Any]				):
          super().test_inference_batch_single_identical(expected_max_diff=3E-3				)







      def       snake_case	(     self					: Optional[Any]				):
          pass







      def       snake_case	(     self					: str				):
          __UpperCamelCase  		=						self.get_dummy_components()
          __UpperCamelCase  		=						StableDiffusionXLImgaImgPipeline(**snake_case				)
          __UpperCamelCase  		=						sd_pipe.to(snake_case				)
          __UpperCamelCase  		=						sd_pipe.to(snake_case				)
          sd_pipe.set_progress_bar_config(disable=snake_case				)

          # forward without prompt embeds
          __UpperCamelCase  		=						self.get_dummy_inputs(snake_case				)
          __UpperCamelCase  		=						3 * ['''this is a negative prompt''']
          __UpperCamelCase  		=						negative_prompt
          __UpperCamelCase  		=						3 * [inputs['''prompt''']]

          __UpperCamelCase  		=						sd_pipe(**snake_case				)
          __UpperCamelCase  		=						output.images[0, -3:, -3:, -1]

          # forward with prompt embeds
          __UpperCamelCase  		=						self.get_dummy_inputs(snake_case				)
          __UpperCamelCase  		=						3 * ['''this is a negative prompt''']
          __UpperCamelCase  		=						3 * [inputs.pop('''prompt'''				)]

          (
              (
              __UpperCamelCase
          )		,      (
              __UpperCamelCase
          )		,      (
              __UpperCamelCase
          )		,      (
              __UpperCamelCase
          )		,      
          )  		=						sd_pipe.encode_prompt(snake_case ,       negative_prompt=snake_case				)

          __UpperCamelCase  		=						sd_pipe(
              **snake_case ,       prompt_embeds=snake_case ,       negative_prompt_embeds=snake_case ,       pooled_prompt_embeds=snake_case ,       negative_pooled_prompt_embeds=snake_case ,       )
          __UpperCamelCase  		=						output.images[0, -3:, -3:, -1]

          # make sure that it's equal
          assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()				).max() < 1E-4







@slow
@require_torch_gpu
class  _lowerCamelCase						(			unittest.TestCase   ):

      """simple docstring"""

      def       snake_case	(     self					: Optional[Any]				):
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()







      def       snake_case	(     self					: str ,       snake_case					: List[Any] ,       snake_case					: str="cpu" ,       snake_case					: int=torch.floataa ,       snake_case					: Optional[int]=0				):
          __UpperCamelCase  		=						torch.Generator(device=snake_case				).manual_seed(snake_case				)
          __UpperCamelCase  		=						np.random.RandomState(snake_case				).standard_normal((1, 4, 64, 64)				)
          __UpperCamelCase  		=						torch.from_numpy(snake_case				).to(device=snake_case ,       dtype=snake_case				)
          __UpperCamelCase  		=						{
              '''prompt''': '''a photograph of an astronaut riding a horse''',
              '''latents''': latents,
              '''generator''': generator,
              '''num_inference_steps''': 3,
              '''guidance_scale''': 7.5,
              '''output_type''': '''numpy''',
          }
          return inputs







      def       snake_case	(     self					: Dict				):
          __UpperCamelCase  		=						DiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-base'''				)
          pipe.to(snake_case				)
          pipe.set_progress_bar_config(disable=snake_case				)

          __UpperCamelCase  		=						self.get_inputs(snake_case				)
          __UpperCamelCase  		=						pipe(**snake_case				).images
          __UpperCamelCase  		=						image[0, -3:, -3:, -1].flatten()

          assert image.shape == (1, 512, 512, 3)
          __UpperCamelCase  		=						np.array([0.4_94_93, 0.4_78_96, 0.4_07_98, 0.5_42_14, 0.5_32_12, 0.4_82_02, 0.4_76_56, 0.4_63_29, 0.4_85_06]				)
          assert np.abs(image_slice - expected_slice				).max() < 7E-3






