





import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel

from ...models.attention import BasicTransformerBlock
from ...utils import logging


UpperCAmelCase_           =	logging.get_logger(__name__)  # pylint: disable=invalid-name




class 			__UpperCamelCase    (							A__    ):





     def __init__(     self				,							_UpperCamelCase				,							_UpperCamelCase=768					):
        super().__init__(_UpperCamelCase					)
        _UpperCAmelCase           =   proj_size

        _UpperCAmelCase           =   CLIPVisionModel(_UpperCamelCase					)
        _UpperCAmelCase           =   PaintByExampleMapper(_UpperCamelCase					)
        _UpperCAmelCase           =   nn.LayerNorm(config.hidden_size					)
        _UpperCAmelCase           =   nn.Linear(config.hidden_size				,							self.proj_size					)

        # uncondition for scaling
        _UpperCAmelCase           =   nn.Parameter(torch.randn((1, 1, self.proj_size)					)					)





     def 		UpperCamelCase(     self				,							_UpperCamelCase				,							_UpperCamelCase=False					):
        _UpperCAmelCase           =   self.model(pixel_values=_UpperCamelCase					)
        _UpperCAmelCase           =   clip_output.pooler_output
        _UpperCAmelCase           =   self.mapper(latent_states[:, None]					)
        _UpperCAmelCase           =   self.final_layer_norm(_UpperCamelCase					)
        _UpperCAmelCase           =   self.proj_out(_UpperCamelCase					)
        if return_uncond_vector:
           return latent_states, self.uncond_vector

        return latent_states




class 			__UpperCamelCase    (							nn.Module    ):





     def __init__(     self				,							_UpperCamelCase					):
        super().__init__()
        _UpperCAmelCase           =   (config.num_hidden_layers + 1) // 5
        _UpperCAmelCase           =   config.hidden_size
        _UpperCAmelCase           =   1
        _UpperCAmelCase           =   nn.ModuleList(
            [
                BasicTransformerBlock(_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase				,							activation_fn='''gelu'''				,							attention_bias=_UpperCamelCase					)
                for _ in range(_UpperCamelCase					)
            ]					)





     def 		UpperCamelCase(     self				,							_UpperCamelCase					):
        for block in self.blocks:
           _UpperCAmelCase           =   block(_UpperCamelCase					)

        return hidden_states





'''simple docstring'''

import argparse
from collections import defaultdict

import yaml


a    :							List[Any]										=    'docs/source/en/_toctree.yml'


def 						__magic_name__       (      __UpperCAmelCase      )						->   str:

					'''simple docstring'''

					snake_case_		     =							defaultdict(__UpperCAmelCase      )
					for doc in model_doc:
										counts[doc["local"]] += 1
					snake_case_		     =							[key for key, value in counts.items() if value > 1]

					snake_case_		     =							[]
					for duplicate_key in duplicates:
										snake_case_		     =							list({doc['''title'''] for doc in model_doc if doc['''local'''] == duplicate_key}      )
										if len(__UpperCAmelCase      ) > 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 model_doc if counts[doc['''local''']] == 1]      )

					# Sort
					return sorted(__UpperCAmelCase, key=lambda __UpperCAmelCase      : s["title"].lower()      )


def 						__magic_name__       (      __UpperCAmelCase=False      )						->   List[Any]:

					'''simple docstring'''

					with open(__UpperCAmelCase, encoding='''utf-8'''      ) as f:
										snake_case_		     =							yaml.safe_load(f.read()      )

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

					# Then to the model doc
					snake_case_		     =							0
					while api_doc[model_idx]["title"] != "Models":
										model_idx += 1

					snake_case_		     =							api_doc[model_idx]['''sections''']

					snake_case_		     =							[(idx, section) for idx, section in enumerate(__UpperCAmelCase      ) if '''sections''' in section]
					snake_case_		     =							False
					for idx, modality_doc in modalities_docs:
										snake_case_		     =							modality_doc['''sections''']
										snake_case_		     =							clean_model_doc_toc(__UpperCAmelCase      )

										if old_modality_doc != new_modality_doc:
															snake_case_		     =							True
															if overwrite:
																				snake_case_		     =							new_modality_doc

					if diff:
										if overwrite:
															snake_case_		     =							model_doc
															snake_case_		     =							api_doc
															with open(__UpperCAmelCase, '''w''', encoding='''utf-8'''      ) as f:
																				f.write(yaml.dump(__UpperCAmelCase, allow_unicode=__UpperCAmelCase      )      )
										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    :							Dict										=    argparse.ArgumentParser()
							parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
							a    :							Union[str, Any]										=    parser.parse_args()

							check_model_doc(args.fix_and_overwrite)

