'''simple docstring'''
import enum
import os
from hashlib import shaaaa
from typing import Optional

from .. import config
from .logging import get_logger


a=  get_logger(__name__)






class 			__lowercase		(						enum.Enum						):







     """simple docstring"""






     SCREAMING_SNAKE_CASE__		   =						'''all_checks'''
     SCREAMING_SNAKE_CASE__		   =						'''basic_checks'''
     SCREAMING_SNAKE_CASE__		   =						'''no_checks'''






class 			__lowercase		(						_lowerCamelCase						):







     """simple docstring"""






     pass






class 			__lowercase		(						_lowerCamelCase						):







     """simple docstring"""






     pass






class 			__lowercase		(						_lowerCamelCase						):







     """simple docstring"""






     pass






class 			__lowercase		(						_lowerCamelCase						):







     """simple docstring"""






     pass







def  _UpperCamelCase (     _a	:	List[Any]				,     _a	:	Dict				,     _a	:	Optional[int]=None			):




    """simple docstring"""



    if expected_checksums is None:
        logger.info('Unable to verify checksums.'			)
        return
    if len(set(__UpperCamelCase			) - set(__UpperCamelCase			)			) > 0:
        raise ExpectedMoreDownloadedFiles(str(set(__UpperCamelCase			) - set(__UpperCamelCase			)			)			)
    if len(set(__UpperCamelCase			) - set(__UpperCamelCase			)			) > 0:
        raise UnexpectedDownloadedFile(str(set(__UpperCamelCase			) - set(__UpperCamelCase			)			)			)
    __UpperCamelCase	:	str		    =  [url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]]
    __UpperCamelCase	:	Any		    =  ' for ' + verification_name if verification_name is not None else ''
    if len(__UpperCamelCase			) > 0:
        raise NonMatchingChecksumError(
            f"""Checksums didn't match{for_verification_name}:\n"""
            f"""{bad_urls}\n"""
            'Set `verification_mode=\'no_checks\'` to skip checksums verification and ignore this error'			)
    logger.info('All the checksums matched successfully' + for_verification_name			)






class 			__lowercase		(						_lowerCamelCase						):







     """simple docstring"""






     pass






class 			__lowercase		(						_lowerCamelCase						):







     """simple docstring"""






     pass






class 			__lowercase		(						_lowerCamelCase						):







     """simple docstring"""






     pass






class 			__lowercase		(						_lowerCamelCase						):







     """simple docstring"""






     pass







def  _UpperCamelCase (     _a	:	Any				,     _a	:	str			):




    """simple docstring"""



    if expected_splits is None:
        logger.info('Unable to verify splits sizes.'			)
        return
    if len(set(__UpperCamelCase			) - set(__UpperCamelCase			)			) > 0:
        raise ExpectedMoreSplits(str(set(__UpperCamelCase			) - set(__UpperCamelCase			)			)			)
    if len(set(__UpperCamelCase			) - set(__UpperCamelCase			)			) > 0:
        raise UnexpectedSplits(str(set(__UpperCamelCase			) - set(__UpperCamelCase			)			)			)
    __UpperCamelCase	:	str		    =  [
        {'expected': expected_splits[name], 'recorded': recorded_splits[name]}
        for name in expected_splits
        if expected_splits[name].num_examples != recorded_splits[name].num_examples
    ]
    if len(__UpperCamelCase			) > 0:
        raise NonMatchingSplitsSizesError(str(__UpperCamelCase			)			)
    logger.info('All the splits matched successfully.'			)







def  _UpperCamelCase (     _a	:	Tuple				,     _a	:	int = True			):




    """simple docstring"""



    if record_checksum:
        __UpperCamelCase	:	List[str]		    =  shaaaa()
        with open(__UpperCamelCase				,     'rb'			) as f:
            for chunk in iter(lambda: f.read(1 << 2_0			)				,     b''			):
                m.update(__UpperCamelCase			)
            __UpperCamelCase	:	Any		    =  m.hexdigest()
    else:
        __UpperCamelCase	:	Tuple		    =  None
    return {"num_bytes": os.path.getsize(__UpperCamelCase			), "checksum": checksum}







def  _UpperCamelCase (     _a	:	Union[str, Any]			):




    """simple docstring"""



    if dataset_size and config.IN_MEMORY_MAX_SIZE:
        return dataset_size < config.IN_MEMORY_MAX_SIZE
    else:
        return False




'''simple docstring'''
import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin






class 			__lowercase		(						_lowerCamelCase			,							_lowerCamelCase						):







     """simple docstring"""






     @register_to_config
     def __init__(		self     ,			*,
         _lowerCamelCase = 4     ,			_lowerCamelCase = 7_6_8     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			):
         super().__init__()

         __UpperCamelCase	:	Optional[int]		    =  nn.Parameter(torch.zeros(_lowerCamelCase    )    )

         # parameters for additional clip time embeddings
         __UpperCamelCase	:	Dict		    =  nn.Linear(_lowerCamelCase     ,			_lowerCamelCase    )
         __UpperCamelCase	:	int		    =  nn.Linear(_lowerCamelCase     ,			_lowerCamelCase    )

         # parameters for encoder hidden states
         __UpperCamelCase	:	Optional[Any]		    =  clip_extra_context_tokens
         __UpperCamelCase	:	Dict		    =  nn.Linear(
             _lowerCamelCase     ,			self.clip_extra_context_tokens * cross_attention_dim    )
         __UpperCamelCase	:	str		    =  nn.Linear(_lowerCamelCase     ,			_lowerCamelCase    )
         __UpperCamelCase	:	List[str]		    =  nn.LayerNorm(_lowerCamelCase    )







     def lowerCAmelCase     (		self     ,			*, _lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase    ):
         if do_classifier_free_guidance:
             # Add the classifier free guidance embeddings to the image embeddings
             __UpperCamelCase	:	int		    =  image_embeddings.shape[0]
             __UpperCamelCase	:	Optional[Any]		    =  self.learned_classifier_free_guidance_embeddings.unsqueeze(0    )
             __UpperCamelCase	:	List[Any]		    =  classifier_free_guidance_embeddings.expand(
                 _lowerCamelCase     ,			-1    )
             __UpperCamelCase	:	Optional[Any]		    =  torch.cat([classifier_free_guidance_embeddings, image_embeddings]     ,			dim=0    )

         # The image embeddings batch size and the text embeddings batch size are equal
         assert image_embeddings.shape[0] == prompt_embeds.shape[0]

         __UpperCamelCase	:	Optional[Any]		    =  prompt_embeds.shape[0]

         # "Specifically, we modify the architecture described in Nichol et al. (2021) by projecting and
         # adding CLIP embeddings to the existing timestep embedding, ...
         __UpperCamelCase	:	Optional[int]		    =  self.embedding_proj(_lowerCamelCase    )
         __UpperCamelCase	:	List[str]		    =  self.clip_image_embeddings_project_to_time_embeddings(_lowerCamelCase    )
         __UpperCamelCase	:	Optional[int]		    =  time_projected_image_embeddings + time_projected_prompt_embeds

         # ... and by projecting CLIP embeddings into four
         # extra tokens of context that are concatenated to the sequence of outputs from the GLIDE text encoder"
         __UpperCamelCase	:	Optional[int]		    =  self.clip_extra_context_tokens_proj(_lowerCamelCase    )
         __UpperCamelCase	:	Tuple		    =  clip_extra_context_tokens.reshape(_lowerCamelCase     ,			-1     ,			self.clip_extra_context_tokens    )
         __UpperCamelCase	:	Dict		    =  clip_extra_context_tokens.permute(0     ,			2     ,			1    )

         __UpperCamelCase	:	Union[str, Any]		    =  self.encoder_hidden_states_proj(_lowerCamelCase    )
         __UpperCamelCase	:	Optional[Any]		    =  self.text_encoder_hidden_states_norm(_lowerCamelCase    )
         __UpperCamelCase	:	Tuple		    =  torch.cat([clip_extra_context_tokens, text_encoder_hidden_states]     ,			dim=1    )

         return text_encoder_hidden_states, additive_clip_time_embeddings



