


'''simple docstring'''

import logging
import os
from typing import List, TextIO, Union

from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask


a_  :				Optional[Any]						= logging.getLogger(__name__)





class      snake_case    (    lowercase			):





  """simple docstring"""




  def __init__(    self			,  UpperCamelCase=-1     ):







   """simple docstring"""
   # in NER datasets, the last column is usually reserved for NER label
   lowerCamelCase_      =  label_idx






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase     ):







   """simple docstring"""
   if isinstance(UpperCamelCase			,  UpperCamelCase     ):
    lowerCamelCase_      =  mode.value
   lowerCamelCase_      =  os.path.join(UpperCamelCase			,  f'''{mode}.txt'''     )
   lowerCamelCase_      =  1
   lowerCamelCase_      =  []
   with open(UpperCamelCase			,  encoding="utf-8"     ) as f:
    lowerCamelCase_      =  []
    lowerCamelCase_      =  []
    for line in f:
     if line.startswith("-DOCSTART-"     ) or line == "" or line == "\n":
      if words:
       examples.append(InputExample(guid=f'''{mode}-{guid_index}'''			,  words=UpperCamelCase			,  labels=UpperCamelCase     )     )
       guid_index += 1
       lowerCamelCase_      =  []
       lowerCamelCase_      =  []
     else:
      lowerCamelCase_      =  line.split(" "     )
      words.append(splits[0]     )
      if len(UpperCamelCase     ) > 1:
       labels.append(splits[self.label_idx].replace("\n"			,  ""     )     )
      else:
       # Examples could have no label for mode = "test"
       labels.append("O"     )
    if words:
     examples.append(InputExample(guid=f'''{mode}-{guid_index}'''			,  words=UpperCamelCase			,  labels=UpperCamelCase     )     )
   return examples






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  0
   for line in test_input_reader:
    if line.startswith("-DOCSTART-"     ) or line == "" or line == "\n":
     writer.write(UpperCamelCase     )
     if not preds_list[example_id]:
      example_id += 1
    elif preds_list[example_id]:
     lowerCamelCase_      =  line.split()[0] + " " + preds_list[example_id].pop(0     ) + "\n"
     writer.write(UpperCamelCase     )
    else:
     logger.warning("Maximum sequence length exceeded: No prediction for '%s'."			,  line.split()[0]     )







  def   snake_case		(    self			,  UpperCamelCase     ):







   """simple docstring"""
   if path:
    with open(UpperCamelCase			,  "r"     ) as f:
     lowerCamelCase_      =  f.read().splitlines()
    if "O" not in labels:
     lowerCamelCase_      =  ["O"] + labels
    return labels
   else:
    return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]





class      snake_case    (    lowercase			):





  """simple docstring"""




  def __init__(    self     ):







   """simple docstring"""
   # in CONLL2003 dataset chunk column is second-to-last
   super().__init__(label_idx=-2     )







  def   snake_case		(    self			,  UpperCamelCase     ):







   """simple docstring"""
   if path:
    with open(UpperCamelCase			,  "r"     ) as f:
     lowerCamelCase_      =  f.read().splitlines()
    if "O" not in labels:
     lowerCamelCase_      =  ["O"] + labels
    return labels
   else:
    return [
        "O",
        "B-ADVP",
        "B-INTJ",
        "B-LST",
        "B-PRT",
        "B-NP",
        "B-SBAR",
        "B-VP",
        "B-ADJP",
        "B-CONJP",
        "B-PP",
        "I-ADVP",
        "I-INTJ",
        "I-LST",
        "I-PRT",
        "I-NP",
        "I-SBAR",
        "I-VP",
        "I-ADJP",
        "I-CONJP",
        "I-PP",
    ]





class      snake_case    (    lowercase			):





  """simple docstring"""




  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase     ):







   """simple docstring"""
   if isinstance(UpperCamelCase			,  UpperCamelCase     ):
    lowerCamelCase_      =  mode.value
   lowerCamelCase_      =  os.path.join(UpperCamelCase			,  f'''{mode}.txt'''     )
   lowerCamelCase_      =  1
   lowerCamelCase_      =  []

   with open(UpperCamelCase			,  encoding="utf-8"     ) as f:
    for sentence in parse_incr(UpperCamelCase     ):
     lowerCamelCase_      =  []
     lowerCamelCase_      =  []
     for token in sentence:
      words.append(token["form"]     )
      labels.append(token["upos"]     )
     assert len(UpperCamelCase     ) == len(UpperCamelCase     )
     if words:
      examples.append(InputExample(guid=f'''{mode}-{guid_index}'''			,  words=UpperCamelCase			,  labels=UpperCamelCase     )     )
      guid_index += 1
   return examples






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  0
   for sentence in parse_incr(UpperCamelCase     ):
    lowerCamelCase_      =  preds_list[example_id]
    lowerCamelCase_      =  ""
    for token in sentence:
     out += f'''{token["form"]} ({token["upos"]}|{s_p.pop(0     )}) '''
    out += "\n"
    writer.write(UpperCamelCase     )
    example_id += 1







  def   snake_case		(    self			,  UpperCamelCase     ):







   """simple docstring"""
   if path:
    with open(UpperCamelCase			,  "r"     ) as f:
     return f.read().splitlines()
   else:
    return [
        "ADJ",
        "ADP",
        "ADV",
        "AUX",
        "CCONJ",
        "DET",
        "INTJ",
        "NOUN",
        "NUM",
        "PART",
        "PRON",
        "PROPN",
        "PUNCT",
        "SCONJ",
        "SYM",
        "VERB",
        "X",
    ]







'''simple docstring'''

import gc
import unittest

import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    PriorTransformer,
    StableUnCLIPPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.testing_utils import enable_full_determinism, load_numpy, require_torch_gpu, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
    assert_mean_pixel_difference,
)


enable_full_determinism()





class      snake_case    (    lowercase			,						lowercase			,						lowercase			,						unittest.TestCase			):





  """simple docstring"""




  _lowerCamelCase		    =  StableUnCLIPPipeline
  _lowerCamelCase		    =  TEXT_TO_IMAGE_PARAMS
  _lowerCamelCase		    =  TEXT_TO_IMAGE_BATCH_PARAMS
  _lowerCamelCase		    =  TEXT_TO_IMAGE_IMAGE_PARAMS
  _lowerCamelCase		    =  TEXT_TO_IMAGE_IMAGE_PARAMS

  # TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false
  _lowerCamelCase		    =  False






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  32
   lowerCamelCase_      =  embedder_hidden_size

   # prior components

   torch.manual_seed(0     )
   lowerCamelCase_      =  CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"     )

   torch.manual_seed(0     )
   lowerCamelCase_      =  CLIPTextModelWithProjection(
       CLIPTextConfig(
           bos_token_id=0			,  eos_token_id=2			,  hidden_size=UpperCamelCase			,  projection_dim=UpperCamelCase			,  intermediate_size=37			,  layer_norm_eps=1e-05			,  num_attention_heads=4			,  num_hidden_layers=5			,  pad_token_id=1			,  vocab_size=1000			,  )     )

   torch.manual_seed(0     )
   lowerCamelCase_      =  PriorTransformer(
       num_attention_heads=2			,  attention_head_dim=12			,  embedding_dim=UpperCamelCase			,  num_layers=1			,  )

   torch.manual_seed(0     )
   lowerCamelCase_      =  DDPMScheduler(
       variance_type="fixed_small_log"			,  prediction_type="sample"			,  num_train_timesteps=1000			,  clip_sample=UpperCamelCase			,  clip_sample_range=5.0			,  beta_schedule="squaredcos_cap_v2"			,  )

   # regular denoising components

   torch.manual_seed(0     )
   lowerCamelCase_      =  StableUnCLIPImageNormalizer(embedding_dim=UpperCamelCase     )
   lowerCamelCase_      =  DDPMScheduler(beta_schedule="squaredcos_cap_v2"     )

   torch.manual_seed(0     )
   lowerCamelCase_      =  CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"     )

   torch.manual_seed(0     )
   lowerCamelCase_      =  CLIPTextModel(
       CLIPTextConfig(
           bos_token_id=0			,  eos_token_id=2			,  hidden_size=UpperCamelCase			,  projection_dim=32			,  intermediate_size=37			,  layer_norm_eps=1e-05			,  num_attention_heads=4			,  num_hidden_layers=5			,  pad_token_id=1			,  vocab_size=1000			,  )     )

   torch.manual_seed(0     )
   lowerCamelCase_      =  UNetaDConditionModel(
       sample_size=32			,  in_channels=4			,  out_channels=4			,  down_block_types=("CrossAttnDownBlock2D", "DownBlock2D")			,  up_block_types=("UpBlock2D", "CrossAttnUpBlock2D")			,  block_out_channels=(32, 64)			,  attention_head_dim=(2, 4)			,  class_embed_type="projection"			,  projection_class_embeddings_input_dim=embedder_projection_dim * 2			,  cross_attention_dim=UpperCamelCase			,  layers_per_block=1			,  upcast_attention=UpperCamelCase			,  use_linear_projection=UpperCamelCase			,  )

   torch.manual_seed(0     )
   lowerCamelCase_      =  DDIMScheduler(
       beta_schedule="scaled_linear"			,  beta_start=0.00_085			,  beta_end=0.012			,  prediction_type="v_prediction"			,  set_alpha_to_one=UpperCamelCase			,  steps_offset=1			,  )

   torch.manual_seed(0     )
   lowerCamelCase_      =  AutoencoderKL()

   lowerCamelCase_      =  {
       # prior components
       "prior_tokenizer": prior_tokenizer,
       "prior_text_encoder": prior_text_encoder,
       "prior": prior,
       "prior_scheduler": prior_scheduler,
       # image noising components
       "image_normalizer": image_normalizer,
       "image_noising_scheduler": image_noising_scheduler,
       # regular denoising components
       "tokenizer": tokenizer,
       "text_encoder": text_encoder,
       "unet": unet,
       "scheduler": scheduler,
       "vae": vae,
   }

   return components






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase=0     ):







   """simple docstring"""
   if str(UpperCamelCase     ).startswith("mps"     ):
    lowerCamelCase_      =  torch.manual_seed(UpperCamelCase     )
   else:
    lowerCamelCase_      =  torch.Generator(device=UpperCamelCase     ).manual_seed(UpperCamelCase     )
   lowerCamelCase_      =  {
       "prompt": "A painting of a squirrel eating a burger",
       "generator": generator,
       "num_inference_steps": 2,
       "prior_num_inference_steps": 2,
       "output_type": "numpy",
   }
   return inputs






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  torch_device == "cpu"

   self._test_attention_slicing_forward_pass(test_max_difference=UpperCamelCase     )







  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  torch_device in ["cpu", "mps"]

   self._test_inference_batch_single_identical(test_max_difference=UpperCamelCase     )





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





  """simple docstring"""




  def   snake_case		(    self     ):







   """simple docstring"""
   # clean up the VRAM after each test
   super().tearDown()
   gc.collect()
   torch.cuda.empty_cache()






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  load_numpy(
       "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy"     )

   lowerCamelCase_      =  StableUnCLIPPipeline.from_pretrained("fusing/stable-unclip-2-1-l"			,  torch_dtype=torch.floataa     )
   pipe.to(UpperCamelCase     )
   pipe.set_progress_bar_config(disable=UpperCamelCase     )
   # stable unclip will oom when integration tests are run on a V100,
   # so turn on memory savings
   pipe.enable_attention_slicing()
   pipe.enable_sequential_cpu_offload()

   lowerCamelCase_      =  torch.Generator(device="cpu"     ).manual_seed(0     )
   lowerCamelCase_      =  pipe("anime turle"			,  generator=UpperCamelCase			,  output_type="np"     )

   lowerCamelCase_      =  output.images[0]

   assert image.shape == (768, 768, 3)

   assert_mean_pixel_difference(UpperCamelCase			,  UpperCamelCase     )







  def   snake_case		(    self     ):







   """simple docstring"""
   torch.cuda.empty_cache()
   torch.cuda.reset_max_memory_allocated()
   torch.cuda.reset_peak_memory_stats()

   lowerCamelCase_      =  StableUnCLIPPipeline.from_pretrained("fusing/stable-unclip-2-1-l"			,  torch_dtype=torch.floataa     )
   lowerCamelCase_      =  pipe.to(UpperCamelCase     )
   pipe.set_progress_bar_config(disable=UpperCamelCase     )
   pipe.enable_attention_slicing()
   pipe.enable_sequential_cpu_offload()

   lowerCamelCase_      =  pipe(
       "anime turtle"			,  prior_num_inference_steps=2			,  num_inference_steps=2			,  output_type="np"			,  )

   lowerCamelCase_      =  torch.cuda.max_memory_allocated()
   # make sure that less than 7 GB is allocated
   assert mem_bytes < 7 * 10**9



