



import argparse
from collections import defaultdict

import yaml


A__	      =       """docs/source/en/_toctree.yml"""


def 		_UpperCAmelCase				(      snake_case			):





					"""simple docstring"""

					_lowerCAmelCase						=    defaultdict(snake_case			)
					_lowerCAmelCase						=    []
					_lowerCAmelCase						=    []
					for doc in doc_list:
										if "local" in doc:
															counts[doc["local"]] += 1

										if doc["title"].lower() == "overview":
															overview_doc.append({"""local""": doc["""local"""], """title""": doc["""title"""]}			)
										else:
															new_doc_list.append(snake_case			)

					_lowerCAmelCase						=    new_doc_list
					_lowerCAmelCase						=    [key for key, value in counts.items() if value > 1]

					_lowerCAmelCase						=    []
					for duplicate_key in duplicates:
										_lowerCAmelCase						=    list({doc["""title"""] for doc in doc_list if doc["""local"""] == duplicate_key}			)
										if len(snake_case			) > 1:
															raise ValueError(
															    F'{duplicate_key} is present several times in the documentation table of content at '
															    """`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the """
															    """others."""			)
										# Only add this once
										new_doc.append({"""local""": duplicate_key, """title""": titles[0]}			)

					# Add none duplicate-keys
					new_doc.extend([doc for doc in doc_list if """local""" not in counts or counts[doc["""local"""]] == 1]			)
					_lowerCAmelCase						=    sorted(snake_case	,						key=lambda snake_case			: s["title"].lower()			)

					# "overview" gets special treatment and is always first
					if len(snake_case			) > 1:
										raise ValueError("""{doc_list} has two 'overview' docs which is not allowed."""			)

					overview_doc.extend(snake_case			)

					# Sort
					return overview_doc


def 		_UpperCAmelCase				(      snake_case=False			):





					"""simple docstring"""

					with open(snake_case	,						encoding="""utf-8"""			) as f:
										_lowerCAmelCase						=    yaml.safe_load(f.read()			)

					# Get to the API doc
					_lowerCAmelCase						=    0
					while content[api_idx]["title"] != "API":
										api_idx += 1
					_lowerCAmelCase						=    content[api_idx]["""sections"""]

					# Then to the model doc
					_lowerCAmelCase						=    0
					while api_doc[scheduler_idx]["title"] != "Schedulers":
										scheduler_idx += 1

					_lowerCAmelCase						=    api_doc[scheduler_idx]["""sections"""]
					_lowerCAmelCase						=    clean_doc_toc(snake_case			)

					_lowerCAmelCase						=    False
					if new_scheduler_doc != scheduler_doc:
										_lowerCAmelCase						=    True
										if overwrite:
															_lowerCAmelCase						=    new_scheduler_doc

					if diff:
										if overwrite:
															_lowerCAmelCase						=    api_doc
															with open(snake_case	,						"""w"""	,						encoding="""utf-8"""			) as f:
																				f.write(yaml.dump(snake_case	,						allow_unicode=snake_case			)			)
										else:
															raise ValueError(
															    """The model doc part of the table of content is not properly sorted, run `make style` to fix this."""			)


def 		_UpperCAmelCase				(      snake_case=False			):





					"""simple docstring"""

					with open(snake_case	,						encoding="""utf-8"""			) as f:
										_lowerCAmelCase						=    yaml.safe_load(f.read()			)

					# Get to the API doc
					_lowerCAmelCase						=    0
					while content[api_idx]["title"] != "API":
										api_idx += 1
					_lowerCAmelCase						=    content[api_idx]["""sections"""]

					# Then to the model doc
					_lowerCAmelCase						=    0
					while api_doc[pipeline_idx]["title"] != "Pipelines":
										pipeline_idx += 1

					_lowerCAmelCase						=    False
					_lowerCAmelCase						=    api_doc[pipeline_idx]["""sections"""]
					_lowerCAmelCase						=    []

					# sort sub pipeline docs
					for pipeline_doc in pipeline_docs:
										if "section" in pipeline_doc:
															_lowerCAmelCase						=    pipeline_doc["""section"""]
															_lowerCAmelCase						=    clean_doc_toc(snake_case			)
															if overwrite:
																				_lowerCAmelCase						=    new_sub_pipeline_doc
										new_pipeline_docs.append(snake_case			)

					# sort overall pipeline doc
					_lowerCAmelCase						=    clean_doc_toc(snake_case			)

					if new_pipeline_docs != pipeline_docs:
										_lowerCAmelCase						=    True
										if overwrite:
															_lowerCAmelCase						=    new_pipeline_docs

					if diff:
										if overwrite:
															_lowerCAmelCase						=    api_doc
															with open(snake_case	,						"""w"""	,						encoding="""utf-8"""			) as f:
																				f.write(yaml.dump(snake_case	,						allow_unicode=snake_case			)			)
										else:
															raise ValueError(
															    """The model doc part of the table of content is not properly sorted, run `make style` to fix this."""			)


if __name__ == "__main__":
	A__	      =       argparse.ArgumentParser()
	parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
	A__	      =       parser.parse_args()

	check_scheduler_doc(args.fix_and_overwrite)
	check_pipeline_doc(args.fix_and_overwrite)







from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput







class       lowerCamelCase__   (       nn.Module):





     def __init__(self		,     UpperCAmelCase = 1_6		,     UpperCAmelCase = 8_8		,     UpperCAmelCase = None		,     UpperCAmelCase = 1		,     UpperCAmelCase = 0.0		,     UpperCAmelCase = 3_2		,     UpperCAmelCase = None		,     UpperCAmelCase = False		,     UpperCAmelCase = None		,     UpperCAmelCase = None		,     UpperCAmelCase = "geglu"		,     UpperCAmelCase = None		,     )     ->   Any:
      super().__init__()
      _lowercase							      =nn.ModuleList(
          [
              TransformeraDModel(
                  num_attention_heads=UpperCAmelCase		,     attention_head_dim=UpperCAmelCase		,     in_channels=UpperCAmelCase		,     num_layers=UpperCAmelCase		,     dropout=UpperCAmelCase		,     norm_num_groups=UpperCAmelCase		,     cross_attention_dim=UpperCAmelCase		,     attention_bias=UpperCAmelCase		,     sample_size=UpperCAmelCase		,     num_vector_embeds=UpperCAmelCase		,     activation_fn=UpperCAmelCase		,     num_embeds_ada_norm=UpperCAmelCase		,     )
              for _ in range(2       )
          ]       )

      # Variables that can be set by a pipeline:

      # The ratio of transformer1 to transformer2's output states to be combined during inference
      _lowercase							      =0.5

      # The shape of `encoder_hidden_states` is expected to be
      # `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
      _lowercase							      =[7_7, 2_5_7]

      # Which transformer to use to encode which condition.
      # E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])`
      _lowercase							      =[1, 0]





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase=None		,     UpperCAmelCase=None		,     UpperCAmelCase=None		,     UpperCAmelCase = True		,     )     ->   str:
      _lowercase							      =hidden_states

      _lowercase							      =[]
      _lowercase							      =0
      # attention_mask is not used yet
      for i in range(2       ):
       # for each of the two transformers, pass the corresponding condition tokens
       _lowercase							      =encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
       _lowercase							      =self.transformer_index_for_condition[i]
       _lowercase							      =self.transformers[transformer_index](
           UpperCAmelCase		,     encoder_hidden_states=UpperCAmelCase		,     timestep=UpperCAmelCase		,     cross_attention_kwargs=UpperCAmelCase		,     return_dict=UpperCAmelCase		,     )[0]
       encoded_states.append(encoded_state - input_states       )
       tokens_start += self.condition_lengths[i]

      _lowercase							      =encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
      _lowercase							      =output_states + input_states

      if not return_dict:
       return (output_states,)

      return TransformeraDModelOutput(sample=UpperCAmelCase       )

