


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

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


SCREAMING_SNAKE_CASE       :		str         =				logging.getLogger(__name__)






class      UpperCamelCase					(		lowercase__     ):


   '''simple docstring'''







   def __init__(     self   ,							UpperCamelCase_=-1		):
        # in NER datasets, the last column is usually reserved for NER label
        lowercase_       :Dict     =       label_idx
   def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_		):
        if isinstance(UpperCamelCase_   ,							UpperCamelCase_		):
             lowercase_       :Dict     =       mode.value
        lowercase_       :int     =       os.path.join(UpperCamelCase_   ,							f"{mode}.txt"		)
        lowercase_       :Any     =       1
        lowercase_       :str     =       []
        with open(UpperCamelCase_   ,							encoding='''utf-8'''		) as f:
             lowercase_       :List[str]     =       []
             lowercase_       :Dict     =       []
             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
                            lowercase_       :Optional[int]     =       []
                            lowercase_       :Dict     =       []
                  else:
                       lowercase_       :List[str]     =       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 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_		):
        lowercase_       :Dict     =       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]:
                  lowercase_       :Union[str, Any]     =       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 		UpperCamelCase  (     self   ,							UpperCamelCase_		):
        if path:
             with open(UpperCamelCase_   ,							'''r'''		) as f:
                  lowercase_       :Optional[Any]     =       f.read().splitlines()
             if "O" not in labels:
                  lowercase_       :Optional[Any]     =       ['''O'''] + labels
             return labels
        else:
             return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]






class      UpperCamelCase					(		lowercase__     ):


   '''simple docstring'''







   def __init__(     self		):
        # in CONLL2003 dataset chunk column is second-to-last
        super().__init__(label_idx=-2		)




   def 		UpperCamelCase  (     self   ,							UpperCamelCase_		):
        if path:
             with open(UpperCamelCase_   ,							'''r'''		) as f:
                  lowercase_       :str     =       f.read().splitlines()
             if "O" not in labels:
                  lowercase_       :Tuple     =       ['''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      UpperCamelCase					(		lowercase__     ):


   '''simple docstring'''







   def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_		):
        if isinstance(UpperCamelCase_   ,							UpperCamelCase_		):
             lowercase_       :Any     =       mode.value
        lowercase_       :List[Any]     =       os.path.join(UpperCamelCase_   ,							f"{mode}.txt"		)
        lowercase_       :List[str]     =       1
        lowercase_       :List[Any]     =       []

        with open(UpperCamelCase_   ,							encoding='''utf-8'''		) as f:
             for sentence in parse_incr(UpperCamelCase_		):
                  lowercase_       :Tuple     =       []
                  lowercase_       :str     =       []
                  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 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_		):
        lowercase_       :Any     =       0
        for sentence in parse_incr(UpperCamelCase_		):
             lowercase_       :Tuple     =       preds_list[example_id]
             lowercase_       :List[Any]     =       ''''''
             for token in sentence:
                  out += f"{token['form']} ({token['upos']}|{s_p.pop(0		)}) "
             out += "\n"
             writer.write(UpperCamelCase_		)
             example_id += 1




   def 		UpperCamelCase  (     self   ,							UpperCamelCase_		):
        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",
             ]





import gc
import unittest

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

from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings
from diffusers.utils import load_numpy, slow, torch_device
from diffusers.utils.testing_utils import require_torch_gpu


SCREAMING_SNAKE_CASE       :		int         =				False






class      UpperCamelCase					(		unittest.TestCase     ):


   '''simple docstring'''







   def 		UpperCamelCase  (     self		):
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()
   @property
   def 		UpperCamelCase  (     self		):
        return 12
   @property
   def 		UpperCamelCase  (     self		):
        return 12
   @property
   def 		UpperCamelCase  (     self		):
        return 32
   @property
   def 		UpperCamelCase  (     self		):
        torch.manual_seed(0		)
        lowercase_       :List[Any]     =       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   ,							num_vq_embeddings=self.num_embed   ,							vq_embed_dim=3   ,							)
        return model
   @property
   def 		UpperCamelCase  (     self		):
        lowercase_       :Optional[Any]     =       CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''		)
        return tokenizer
   @property
   def 		UpperCamelCase  (     self		):
        torch.manual_seed(0		)
        lowercase_       :int     =       CLIPTextConfig(
            bos_token_id=0   ,							eos_token_id=2   ,							hidden_size=self.text_embedder_hidden_size   ,							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(UpperCamelCase_		)
   @property
   def 		UpperCamelCase  (     self		):
        torch.manual_seed(0		)

        lowercase_       :int     =       12
        lowercase_       :List[Any]     =       12

        lowercase_       :Dict     =       {
            '''attention_bias''': True,
            '''cross_attention_dim''': 32,
            '''attention_head_dim''': height * width,
            '''num_attention_heads''': 1,
            '''num_vector_embeds''': self.num_embed,
            '''num_embeds_ada_norm''': self.num_embeds_ada_norm,
            '''norm_num_groups''': 32,
            '''sample_size''': width,
            '''activation_fn''': '''geglu-approximate''',
        }

        lowercase_       :int     =       TransformeraDModel(**UpperCamelCase_		)
        return model
   def 		UpperCamelCase  (     self		):
        lowercase_       :List[str]     =       '''cpu'''

        lowercase_       :int     =       self.dummy_vqvae
        lowercase_       :int     =       self.dummy_text_encoder
        lowercase_       :Any     =       self.dummy_tokenizer
        lowercase_       :Optional[int]     =       self.dummy_transformer
        lowercase_       :List[str]     =       VQDiffusionScheduler(self.num_embed		)
        lowercase_       :int     =       LearnedClassifierFreeSamplingEmbeddings(learnable=UpperCamelCase_		)

        lowercase_       :List[Any]     =       VQDiffusionPipeline(
            vqvae=UpperCamelCase_   ,							text_encoder=UpperCamelCase_   ,							tokenizer=UpperCamelCase_   ,							transformer=UpperCamelCase_   ,							scheduler=UpperCamelCase_   ,							learned_classifier_free_sampling_embeddings=UpperCamelCase_   ,							)
        lowercase_       :Union[str, Any]     =       pipe.to(UpperCamelCase_		)
        pipe.set_progress_bar_config(disable=UpperCamelCase_		)

        lowercase_       :Dict     =       '''teddy bear playing in the pool'''

        lowercase_       :Dict     =       torch.Generator(device=UpperCamelCase_		).manual_seed(0		)
        lowercase_       :Optional[int]     =       pipe([prompt]   ,							generator=UpperCamelCase_   ,							num_inference_steps=2   ,							output_type='''np'''		)
        lowercase_       :Any     =       output.images

        lowercase_       :Tuple     =       torch.Generator(device=UpperCamelCase_		).manual_seed(0		)
        lowercase_       :str     =       pipe(
            [prompt]   ,							generator=UpperCamelCase_   ,							output_type='''np'''   ,							return_dict=UpperCamelCase_   ,							num_inference_steps=2		)[0]

        lowercase_       :Optional[Any]     =       image[0, -3:, -3:, -1]
        lowercase_       :Tuple     =       image_from_tuple[0, -3:, -3:, -1]

        assert image.shape == (1, 24, 24, 3)

        lowercase_       :str     =       np.array([0.6551, 0.6168, 0.5008, 0.5676, 0.5659, 0.4295, 0.6073, 0.5599, 0.4992]		)

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




   def 		UpperCamelCase  (     self		):
        lowercase_       :int     =       '''cpu'''

        lowercase_       :Dict     =       self.dummy_vqvae
        lowercase_       :str     =       self.dummy_text_encoder
        lowercase_       :List[Any]     =       self.dummy_tokenizer
        lowercase_       :Any     =       self.dummy_transformer
        lowercase_       :Optional[Any]     =       VQDiffusionScheduler(self.num_embed		)
        lowercase_       :List[str]     =       LearnedClassifierFreeSamplingEmbeddings(
            learnable=UpperCamelCase_   ,							hidden_size=self.text_embedder_hidden_size   ,							length=tokenizer.model_max_length		)

        lowercase_       :Optional[int]     =       VQDiffusionPipeline(
            vqvae=UpperCamelCase_   ,							text_encoder=UpperCamelCase_   ,							tokenizer=UpperCamelCase_   ,							transformer=UpperCamelCase_   ,							scheduler=UpperCamelCase_   ,							learned_classifier_free_sampling_embeddings=UpperCamelCase_   ,							)
        lowercase_       :Dict     =       pipe.to(UpperCamelCase_		)
        pipe.set_progress_bar_config(disable=UpperCamelCase_		)

        lowercase_       :int     =       '''teddy bear playing in the pool'''

        lowercase_       :Tuple     =       torch.Generator(device=UpperCamelCase_		).manual_seed(0		)
        lowercase_       :Optional[int]     =       pipe([prompt]   ,							generator=UpperCamelCase_   ,							num_inference_steps=2   ,							output_type='''np'''		)
        lowercase_       :Optional[Any]     =       output.images

        lowercase_       :Optional[int]     =       torch.Generator(device=UpperCamelCase_		).manual_seed(0		)
        lowercase_       :Dict     =       pipe(
            [prompt]   ,							generator=UpperCamelCase_   ,							output_type='''np'''   ,							return_dict=UpperCamelCase_   ,							num_inference_steps=2		)[0]

        lowercase_       :List[str]     =       image[0, -3:, -3:, -1]
        lowercase_       :Optional[int]     =       image_from_tuple[0, -3:, -3:, -1]

        assert image.shape == (1, 24, 24, 3)

        lowercase_       :Dict     =       np.array([0.6693, 0.6075, 0.4959, 0.5701, 0.5583, 0.4333, 0.6171, 0.5684, 0.4988]		)

        assert np.abs(image_slice.flatten() - expected_slice		).max() < 2.0
        assert np.abs(image_from_tuple_slice.flatten() - expected_slice		).max() < 1E-2






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


   '''simple docstring'''







   def 		UpperCamelCase  (     self		):
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()




   def 		UpperCamelCase  (     self		):
        lowercase_       :List[str]     =       load_numpy(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
            '''/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy'''		)

        lowercase_       :int     =       VQDiffusionPipeline.from_pretrained('''microsoft/vq-diffusion-ithq'''		)
        lowercase_       :Tuple     =       pipeline.to(UpperCamelCase_		)
        pipeline.set_progress_bar_config(disable=UpperCamelCase_		)

        # requires GPU generator for gumbel softmax
        # don't use GPU generator in tests though
        lowercase_       :Dict     =       torch.Generator(device=UpperCamelCase_		).manual_seed(0		)
        lowercase_       :int     =       pipeline(
            '''teddy bear playing in the pool'''   ,							num_images_per_prompt=1   ,							generator=UpperCamelCase_   ,							output_type='''np'''   ,							)

        lowercase_       :List[str]     =       output.images[0]

        assert image.shape == (256, 256, 3)
        assert np.abs(expected_image - image		).max() < 2.0

