






from dataclasses import dataclass
from typing import Optional

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin


@dataclass
class 						_a       (  UpperCAmelCase__    ):






  """simple docstring"""





  A_						=   42




class 						_a       (  UpperCAmelCase__						,       UpperCAmelCase__    ):






  """simple docstring"""





  @register_to_config
  def __init__(     self		,		_UpperCAmelCase = 16		,		_UpperCAmelCase = 88		,		_UpperCAmelCase = None		,		_UpperCAmelCase = None		,		_UpperCAmelCase = 1		,		_UpperCAmelCase = 0.0		,		_UpperCAmelCase = 32		,		_UpperCAmelCase = None		,		_UpperCAmelCase = False		,		_UpperCAmelCase = None		,		_UpperCAmelCase = "geglu"		,		_UpperCAmelCase = True		,		_UpperCAmelCase = True		,		)			-> Tuple:
     super().__init__()
     UpperCamelCase_									=     num_attention_heads
     UpperCamelCase_									=     attention_head_dim
     UpperCamelCase_									=     num_attention_heads * attention_head_dim

     UpperCamelCase_									=     in_channels

     UpperCamelCase_									=     torch.nn.GroupNorm(num_groups=_UpperCAmelCase		,		num_channels=_UpperCAmelCase		,		eps=1e-6		,		affine=_UpperCAmelCase )
     UpperCamelCase_									=     nn.Linear(_UpperCAmelCase		,		_UpperCAmelCase )

     # 3. Define transformers blocks
     UpperCamelCase_									=     nn.ModuleList(
         [
             BasicTransformerBlock(
                 _UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		dropout=_UpperCAmelCase		,		cross_attention_dim=_UpperCAmelCase		,		activation_fn=_UpperCAmelCase		,		attention_bias=_UpperCAmelCase		,		double_self_attention=_UpperCAmelCase		,		norm_elementwise_affine=_UpperCAmelCase		,		)
             for d in range(_UpperCAmelCase )
         ] )

     UpperCamelCase_									=     nn.Linear(_UpperCAmelCase		,		_UpperCAmelCase )




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase		,		_UpperCAmelCase=None		,		_UpperCAmelCase=None		,		_UpperCAmelCase=None		,		_UpperCAmelCase=1		,		_UpperCAmelCase=None		,		_UpperCAmelCase = True		,		)			-> Dict:
     UpperCamelCase_		,      UpperCamelCase_		,      UpperCamelCase_		,      UpperCamelCase_									=     hidden_states.shape
     UpperCamelCase_									=     batch_frames // num_frames

     UpperCamelCase_									=     hidden_states

     UpperCamelCase_									=     hidden_states[None, :].reshape(_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase )
     UpperCamelCase_									=     hidden_states.permute(0		,		2		,		1		,		3		,		4 )

     UpperCamelCase_									=     self.norm(_UpperCAmelCase )
     UpperCamelCase_									=     hidden_states.permute(0		,		3		,		4		,		2		,		1 ).reshape(batch_size * height * width		,		_UpperCAmelCase		,		_UpperCAmelCase )

     UpperCamelCase_									=     self.proj_in(_UpperCAmelCase )

     # 2. Blocks
     for block in self.transformer_blocks:
        UpperCamelCase_									=     block(
            _UpperCAmelCase		,		encoder_hidden_states=_UpperCAmelCase		,		timestep=_UpperCAmelCase		,		cross_attention_kwargs=_UpperCAmelCase		,		class_labels=_UpperCAmelCase		,		)

     # 3. Output
     UpperCamelCase_									=     self.proj_out(_UpperCAmelCase )
     UpperCamelCase_									=     (
         hidden_states[None, None, :]
         .reshape(_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase )
         .permute(0		,		3		,		4		,		1		,		2 )
         .contiguous()
     )
     UpperCamelCase_									=     hidden_states.reshape(_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase )

     UpperCamelCase_									=     hidden_states + residual

     if not return_dict:
        return (output,)

     return TransformerTemporalModelOutput(sample=_UpperCAmelCase )













import unittest

from transformers.testing_utils import require_bsa
from transformers.utils import is_bsa_available

from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin


if is_bsa_available():
 from transformers import MarkupLMFeatureExtractor


class 						_a       (  unittest.TestCase    ):






  """simple docstring"""





  def __init__(     self		,		_UpperCAmelCase )			-> List[str]:
     UpperCamelCase_									=     parent




  def    _UpperCAmelCase     (     self )			-> Optional[Any]:
     return {}






def     _snake_case    ():
   UpperCamelCase_									=     '<HTML>\n\n    <HEAD>\n    <TITLE>sample document</TITLE>\n    </HEAD>\n\n    <BODY BGCOLOR="FFFFFF">\n    <HR>\n    <a href="http://google.com">Goog</a>\n    <H1>This is one header</H1>\n    <H2>This is a another Header</H2>\n    <P>Travel from\n        <P>\n        <B>SFO to JFK</B>\n        <BR>\n        <B><I>on May 2, 2015 at 2:00 pm. For details go to confirm.com </I></B>\n        <HR>\n        <div style="color:#0000FF">\n            <h3>Traveler <b> name </b> is\n            <p> John Doe </p>\n        </div>'

   UpperCamelCase_									=     '\n    <!DOCTYPE html>\n    <html>\n    <body>\n\n    <h1>My First Heading</h1>\n    <p>My first paragraph.</p>\n\n    </body>\n    </html>\n    '

   return [html_string_a, html_string_a]


@require_bsa
class 						_a       (  UpperCAmelCase__						,       unittest.TestCase    ):






  """simple docstring"""





  A_						=   MarkupLMFeatureExtractor if is_bsa_available() else None




  def    _UpperCAmelCase     (     self )			-> int:
     UpperCamelCase_									=     MarkupLMFeatureExtractionTester(self )




  @property
  def    _UpperCAmelCase     (     self )			-> Any:
     return self.feature_extract_tester.prepare_feat_extract_dict()




  def    _UpperCAmelCase     (     self )			-> Dict:
     # Initialize feature_extractor
     UpperCamelCase_									=     self.feature_extraction_class()

     # Test not batched input
     UpperCamelCase_									=     get_html_strings()[0]
     UpperCamelCase_									=     feature_extractor(_UpperCAmelCase )

     # fmt: off
     UpperCamelCase_									=     [['sample document', 'Goog', 'This is one header', 'This is a another Header', 'Travel from', 'SFO to JFK', 'on May 2, 2015 at 2:00 pm. For details go to confirm.com', 'Traveler', 'name', 'is', 'John Doe']]
     UpperCamelCase_									=     [['/html/head/title', '/html/body/a', '/html/body/h1', '/html/body/h2', '/html/body/p', '/html/body/p/p/b[1]', '/html/body/p/p/b[2]/i', '/html/body/p/p/div/h3', '/html/body/p/p/div/h3/b', '/html/body/p/p/div/h3', '/html/body/p/p/div/h3/p']]
     # fmt: on

     self.assertEqual(encoding.nodes		,		_UpperCAmelCase )
     self.assertEqual(encoding.xpaths		,		_UpperCAmelCase )

     # Test batched
     UpperCamelCase_									=     get_html_strings()
     UpperCamelCase_									=     feature_extractor(_UpperCAmelCase )

     # fmt: off
     UpperCamelCase_									=     expected_nodes + [['My First Heading', 'My first paragraph.']]
     UpperCamelCase_									=     expected_xpaths + [['/html/body/h1', '/html/body/p']]

     self.assertEqual(len(encoding.nodes )		,		2 )
     self.assertEqual(len(encoding.xpaths )		,		2 )

     self.assertEqual(encoding.nodes		,		_UpperCAmelCase )
     self.assertEqual(encoding.xpaths		,		_UpperCAmelCase )





