'''simple docstring'''





from typing import Optional, Union

from ...file_utils import TensorType
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, TruncationStrategy




class     UpperCamelCase					(  lowercase__					):
  a    :List[str]      					=				"""MarkupLMFeatureExtractor"""
  a    :Tuple      					=				("""MarkupLMTokenizer""", """MarkupLMTokenizerFast""")
  a    :Optional[int]      					=				True



  def __call__( self		,    __lowerCAmelCase=None		,    __lowerCAmelCase=None		,    __lowerCAmelCase=None		,    __lowerCAmelCase=None		,    __lowerCAmelCase=None		,    __lowerCAmelCase = True		,    __lowerCAmelCase = False		,    __lowerCAmelCase = None		,    __lowerCAmelCase = None		,    __lowerCAmelCase = 0		,    __lowerCAmelCase = None		,    __lowerCAmelCase = None		,    __lowerCAmelCase = None		,    __lowerCAmelCase = False		,    __lowerCAmelCase = False		,    __lowerCAmelCase = False		,    __lowerCAmelCase = False		,    __lowerCAmelCase = True		,    __lowerCAmelCase = None		,    **__lowerCAmelCase		,    ):







      '''simple docstring'''


      if self.parse_html:
          if html_strings is None:
              raise ValueError("""Make sure to pass HTML strings in case `parse_html` is set to `True`"""   )

          if nodes is not None or xpaths is not None or node_labels is not None:
              raise ValueError(
                  """Please don\'t pass nodes, xpaths nor node labels in case `parse_html` is set to `True`"""   )

          _a							:       Optional[Any]       					=			self.feature_extractor(_A   )
          _a							:       int       					=			features['nodes']
          _a							:       Union[str, Any]       					=			features['xpaths']
      else:
          if html_strings is not None:
              raise ValueError("""You have passed HTML strings but `parse_html` is set to `False`."""   )
          if nodes is None or xpaths is None:
              raise ValueError("""Make sure to pass nodes and xpaths in case `parse_html` is set to `False`"""   )

        # # second, apply the tokenizer
      if questions is not None and self.parse_html:
          if isinstance(_A		,    _A   ):
              _a							:       Union[str, Any]       					=			[questions]  # add batch dimension (as the feature extractor always adds a batch dimension)

      _a							:       Any       					=			self.tokenizer(
          text=questions if questions is not None else nodes		,    text_pair=nodes if questions is not None else None		,    xpaths=_A		,    node_labels=_A		,    add_special_tokens=_A		,    padding=_A		,    truncation=_A		,    max_length=_A		,    stride=_A		,    pad_to_multiple_of=_A		,    return_token_type_ids=_A		,    return_attention_mask=_A		,    return_overflowing_tokens=_A		,    return_special_tokens_mask=_A		,    return_offsets_mapping=_A		,    return_length=_A		,    verbose=_A		,    return_tensors=_A		,    **_A		,    )

      return encoded_inputs



  def 						a_       ( self		,    *__lowerCAmelCase		,    **__lowerCAmelCase   ):







      '''simple docstring'''


      return self.tokenizer.batch_decode(*_A		,    **_A   )



  def 						a_       ( self		,    *__lowerCAmelCase		,    **__lowerCAmelCase   ):







      '''simple docstring'''


      return self.tokenizer.decode(*_A		,    **_A   )



  @property
  def 						a_       ( self   ):







      '''simple docstring'''


      _a							:       Any       					=			self.tokenizer.model_input_names
      return tokenizer_input_names








'''simple docstring'''





import unittest

import torch

from diffusers import DDIMScheduler, TextToVideoZeroPipeline
from diffusers.utils import load_pt, require_torch_gpu, slow

from ..test_pipelines_common import assert_mean_pixel_difference




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



  def 						a_       ( self   ):







      '''simple docstring'''


      _a							:       List[Any]       					=			"""runwayml/stable-diffusion-v1-5"""
      _a							:       Tuple       					=			TextToVideoZeroPipeline.from_pretrained(__lowerCAmelCase		,    torch_dtype=torch.floataa   ).to("""cuda"""   )
      _a							:       Dict       					=			DDIMScheduler.from_config(pipe.scheduler.config   )
      _a							:       int       					=			torch.Generator(device="""cuda"""   ).manual_seed(0   )

      _a							:       Union[str, Any]       					=			"""A bear is playing a guitar on Times Square"""
      _a							:       List[Any]       					=			pipe(prompt=__lowerCAmelCase		,    generator=__lowerCAmelCase   ).images

      _a							:       Any       					=			load_pt(
          """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text-to-video/A bear is playing a guitar on Times Square.pt"""   )

      assert_mean_pixel_difference(__lowerCAmelCase		,    __lowerCAmelCase   )


