






"""simple docstring"""



import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    TextToVideoSDPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import is_xformers_available, load_numpy, skip_mps, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()





@skip_mps
class 					_UpperCAmelCase       ( _A   ,						unittest.TestCase							):
					SCREAMING_SNAKE_CASE_		:				Tuple							    =   TextToVideoSDPipeline
					SCREAMING_SNAKE_CASE_		:				Optional[Any]							    =   TEXT_TO_IMAGE_PARAMS
					SCREAMING_SNAKE_CASE_		:				Optional[int]							    =   TEXT_TO_IMAGE_BATCH_PARAMS
					# No `output_type`.
					SCREAMING_SNAKE_CASE_		:				Optional[Any]							    =   frozenset(
					    [
					        "num_inference_steps",
					        "generator",
					        "latents",
					        "return_dict",
					        "callback",
					        "callback_steps",
					    ]							)




					def   A  (				self     :						str   )  ->  int:
							torch.manual_seed(0   )
							lowercase_ : List[str]							       =   UNetaDConditionModel(
							    block_out_channels=(32, 64, 64, 64)    ,						layers_per_block=2    ,						sample_size=32    ,						in_channels=4    ,						out_channels=4    ,						down_block_types=('''CrossAttnDownBlock3D''', '''CrossAttnDownBlock3D''', '''CrossAttnDownBlock3D''', '''DownBlock3D''')    ,						up_block_types=('''UpBlock3D''', '''CrossAttnUpBlock3D''', '''CrossAttnUpBlock3D''', '''CrossAttnUpBlock3D''')    ,						cross_attention_dim=32    ,						attention_head_dim=4    ,						)
							lowercase_ : Union[str, Any]							       =   DDIMScheduler(
							    beta_start=0.00085    ,						beta_end=0.012    ,						beta_schedule='''scaled_linear'''    ,						clip_sample=A    ,						set_alpha_to_one=A    ,						)
							torch.manual_seed(0   )
							lowercase_ : str							       =   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=1_28    ,						)
							torch.manual_seed(0   )
							lowercase_ : str							       =   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=10_00    ,						hidden_act='''gelu'''    ,						projection_dim=5_12    ,						)
							lowercase_ : Any							       =   CLIPTextModel(A   )
							lowercase_ : Optional[Any]							       =   CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''   )

							lowercase_ : Tuple							       =   {
							    '''unet''': unet,
							    '''scheduler''': scheduler,
							    '''vae''': vae,
							    '''text_encoder''': text_encoder,
							    '''tokenizer''': tokenizer,
							}
							return components




					def   A  (				self     :						int    ,						A     :						List[str]    ,						A     :						List[str]=0   )  ->  Any:
							if str(A   ).startswith('''mps'''   ):
									lowercase_ : Optional[Any]							       =   torch.manual_seed(A   )
							else:
									lowercase_ : List[Any]							       =   torch.Generator(device=A   ).manual_seed(A   )
							lowercase_ : Union[str, Any]							       =   {
							    '''prompt''': '''A painting of a squirrel eating a burger''',
							    '''generator''': generator,
							    '''num_inference_steps''': 2,
							    '''guidance_scale''': 6.0,
							    '''output_type''': '''pt''',
							}
							return inputs




					def   A  (				self     :						str   )  ->  Optional[Any]:
							lowercase_ : Tuple							       =   '''cpu'''  # ensure determinism for the device-dependent torch.Generator
							lowercase_ : int							       =   self.get_dummy_components()
							lowercase_ : List[Any]							       =   TextToVideoSDPipeline(**A   )
							lowercase_ : Optional[Any]							       =   sd_pipe.to(A   )
							sd_pipe.set_progress_bar_config(disable=A   )

							lowercase_ : int							       =   self.get_dummy_inputs(A   )
							lowercase_ : Optional[Any]							       =   '''np'''
							lowercase_ : Tuple							       =   sd_pipe(**A   ).frames
							lowercase_ : Any							       =   frames[0][-3:, -3:, -1]

							assert frames[0].shape == (64, 64, 3)
							lowercase_ : Tuple							       =   np.array([158.0, 160.0, 153.0, 125.0, 100.0, 121.0, 111.0, 93.0, 113.0]   )

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




					def   A  (				self     :						int   )  ->  int:
							self._test_attention_slicing_forward_pass(test_mean_pixel_difference=A    ,						expected_max_diff=3e-3   )




					@unittest.skipIf(
					    torch_device != '''cuda''' or not is_xformers_available()    ,						reason='''XFormers attention is only available with CUDA and `xformers` installed'''    ,						)
					def   A  (				self     :						List[str]   )  ->  List[str]:
							self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=A    ,						expected_max_diff=1e-2   )




					@unittest.skip(reason='''Batching needs to be properly figured out first for this pipeline.'''   )
					def   A  (				self     :						Dict   )  ->  List[Any]:
							pass




					@unittest.skip(reason='''Batching needs to be properly figured out first for this pipeline.'''   )
					def   A  (				self     :						Tuple   )  ->  Optional[Any]:
							pass




					@unittest.skip(reason='''`num_images_per_prompt` argument is not supported for this pipeline.'''   )
					def   A  (				self     :						Optional[Any]   )  ->  Tuple:
							pass






					def   A  (				self     :						List[str]   )  ->  Tuple:
							return super().test_progress_bar()





@slow
@skip_mps
class 					_UpperCAmelCase       ( unittest.TestCase							):




					def   A  (				self     :						Any   )  ->  Union[str, Any]:
							lowercase_ : Optional[Any]							       =   load_numpy(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_to_video/video.npy'''   )

							lowercase_ : List[str]							       =   TextToVideoSDPipeline.from_pretrained('''damo-vilab/text-to-video-ms-1.7b'''   )
							lowercase_ : List[str]							       =   DPMSolverMultistepScheduler.from_config(pipe.scheduler.config   )
							lowercase_ : List[str]							       =   pipe.to('''cuda'''   )

							lowercase_ : List[str]							       =   '''Spiderman is surfing'''
							lowercase_ : List[Any]							       =   torch.Generator(device='''cpu'''   ).manual_seed(0   )

							lowercase_ : int							       =   pipe(A    ,						generator=A    ,						num_inference_steps=25    ,						output_type='''pt'''   ).frames
							lowercase_ : Union[str, Any]							       =   video_frames.cpu().numpy()

							assert np.abs(expected_video - video   ).mean() < 5e-2






					def   A  (				self     :						Any   )  ->  Dict:
							lowercase_ : int							       =   load_numpy(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_to_video/video_2step.npy'''   )

							lowercase_ : int							       =   TextToVideoSDPipeline.from_pretrained('''damo-vilab/text-to-video-ms-1.7b'''   )
							lowercase_ : List[Any]							       =   pipe.to('''cuda'''   )

							lowercase_ : Optional[Any]							       =   '''Spiderman is surfing'''
							lowercase_ : List[str]							       =   torch.Generator(device='''cpu'''   ).manual_seed(0   )

							lowercase_ : Union[str, Any]							       =   pipe(A    ,						generator=A    ,						num_inference_steps=2    ,						output_type='''pt'''   ).frames
							lowercase_ : Optional[Any]							       =   video_frames.cpu().numpy()

							assert np.abs(expected_video - video   ).mean() < 5e-2











"""simple docstring"""




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

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


_snake_case          =   logging.getLogger(__name__)






class        UpperCamelCase			(	snake_case_      ):


 def __init__(     self		:						Optional[Any]							,						UpperCAmelCase__		:						Optional[int]=-1							)					->       Tuple:
   # in NER datasets, the last column is usually reserved for NER label
   _a  :       Optional[int]             =	label_idx


 def _lowercase	(     self		:						Any							,						UpperCAmelCase__		:						Dict							,						UpperCAmelCase__		:						Union[Split, str]							)					->       List[InputExample]:
   if isinstance(UpperCAmelCase__							,						UpperCAmelCase__							):
     _a  :       Any             =	mode.value
   _a  :       Optional[int]             =	os.path.join(UpperCAmelCase__							,						f"""{mode}.txt"""							)
   _a  :       int             =	1
   _a  :       int             =	[]
   with open(UpperCAmelCase__							,						encoding="""utf-8"""							) as f:
     _a  :       str             =	[]
     _a  :       str             =	[]
     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
           _a  :       List[str]             =	[]
           _a  :       str             =	[]
       else:
         _a  :       List[Any]             =	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 _lowercase	(     self		:						Optional[Any]							,						UpperCAmelCase__		:						TextIO							,						UpperCAmelCase__		:						TextIO							,						UpperCAmelCase__		:						List							)					->       Union[str, Any]:
   _a  :       List[str]             =	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]:
       _a  :       int             =	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 _lowercase	(     self		:						List[Any]							,						UpperCAmelCase__		:						str							)					->       List[str]:
   if path:
     with open(UpperCAmelCase__							,						"""r"""							) as f:
       _a  :       List[Any]             =	f.read().splitlines()
     if "O" not in labels:
       _a  :       Union[str, 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			(	snake_case_      ):


 def __init__(     self		:						Union[str, Any]							)					->       List[str]:
   # in CONLL2003 dataset chunk column is second-to-last
   super().__init__(label_idx=-2							)







 def _lowercase	(     self		:						List[Any]							,						UpperCAmelCase__		:						str							)					->       List[str]:
   if path:
     with open(UpperCAmelCase__							,						"""r"""							) as f:
       _a  :       Optional[int]             =	f.read().splitlines()
     if "O" not in labels:
       _a  :       Optional[Any]             =	["""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			(	snake_case_      ):


 def _lowercase	(     self		:						Optional[Any]							,						UpperCAmelCase__		:						List[Any]							,						UpperCAmelCase__		:						Union[Split, str]							)					->       List[InputExample]:
   if isinstance(UpperCAmelCase__							,						UpperCAmelCase__							):
     _a  :       List[Any]             =	mode.value
   _a  :       Union[str, Any]             =	os.path.join(UpperCAmelCase__							,						f"""{mode}.txt"""							)
   _a  :       List[str]             =	1
   _a  :       Optional[Any]             =	[]

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







 def _lowercase	(     self		:						List[str]							,						UpperCAmelCase__		:						str							)					->       List[str]:
   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",
     ]

