





"""simple docstring"""




import os

import pytest

from transformers.dynamic_module_utils import get_imports


a						: Optional[int]     	=			'''
import os
'''

a						: List[Any]     	=			'''
def foo():
    import os
    return False
'''

a						: Tuple     	=			'''
def foo():
    def bar():
        if True:
            import os
        return False
    return bar()
'''

a						: Any     	=			'''
import os

try:
    import bar
except ImportError:
    raise ValueError()
'''

a						: List[str]     	=			'''
import os

def foo():
    try:
        import bar
    except ImportError:
        raise ValueError()
'''

a						: Any     	=			'''
import os

try:
    import bar
except (ImportError, AttributeError):
    raise ValueError()
'''

a						: Union[str, Any]     	=			'''
import os

try:
    import bar
except ImportError as e:
    raise ValueError()
'''

a						: Optional[Any]     	=			'''
import os

try:
    import bar
except:
    raise ValueError()
'''

a						: List[Any]     	=			'''
import os

try:
    import bar
    import baz
except ImportError:
    raise ValueError()
'''

a						: Optional[int]     	=			'''
import os

try:
    import bar
    import baz
except ImportError:
    x = 1
    raise ValueError()
'''

a						: Union[str, Any]     	=			[
    TOP_LEVEL_IMPORT,
    IMPORT_IN_FUNCTION,
    DEEPLY_NESTED_IMPORT,
    TOP_LEVEL_TRY_IMPORT,
    GENERIC_EXCEPT_IMPORT,
    MULTILINE_TRY_IMPORT,
    MULTILINE_BOTH_IMPORT,
    MULTIPLE_EXCEPTS_IMPORT,
    EXCEPT_AS_IMPORT,
    TRY_IMPORT_IN_FUNCTION,
]






@pytest.mark.parametrize("case"    ,       _lowercase					)
def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    Dict    ,       _lowercase						:    Tuple					) ->Union[str, Any]:






	'''simple docstring'''






	a      :		List[str]	=				os.path.join(_lowercase    ,       "test_file.py"					)
	with open(_lowercase    ,       "w"					) as _tmp_file:
		_tmp_file.write(_lowercase					)

	a      :		str	=				get_imports(_lowercase					)
	assert parsed_imports == ["os"]









"""simple docstring"""




import torch
from torch import nn

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


class        __UpperCamelCase				(						a__		,       a__						):






							@register_to_config
							def __init__(   self      ,					*,
							    lowerCAmelCase__ = 4      ,					lowerCAmelCase__ = 768      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					)	-> Optional[Any]:
								super().__init__()

								a      :		Tuple	=				nn.Parameter(torch.zeros(lowerCAmelCase__      )      )

								# parameters for additional clip time embeddings
								a      :		str	=				nn.Linear(lowerCAmelCase__      ,					lowerCAmelCase__      )
								a      :		Any	=				nn.Linear(lowerCAmelCase__      ,					lowerCAmelCase__      )

								# parameters for encoder hidden states
								a      :		int	=				clip_extra_context_tokens
								a      :		int	=				nn.Linear(
								    lowerCAmelCase__      ,					self.clip_extra_context_tokens * cross_attention_dim      )
								a      :		Any	=				nn.Linear(lowerCAmelCase__      ,					lowerCAmelCase__      )
								a      :		str	=				nn.LayerNorm(lowerCAmelCase__      )






							def     __a   (   self      ,					*, lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      )	-> Union[str, Any]:
								if do_classifier_free_guidance:
									# Add the classifier free guidance embeddings to the image embeddings
									a      :		str	=				image_embeddings.shape[0]
									a      :		Optional[int]	=				self.learned_classifier_free_guidance_embeddings.unsqueeze(0      )
									a      :		Any	=				classifier_free_guidance_embeddings.expand(
									    lowerCAmelCase__      ,					-1      )
									a      :		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]

								a      :		List[str]	=				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, ...
								a      :		Dict	=				self.embedding_proj(lowerCAmelCase__      )
								a      :		List[str]	=				self.clip_image_embeddings_project_to_time_embeddings(lowerCAmelCase__      )
								a      :		Dict	=				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"
								a      :		Union[str, Any]	=				self.clip_extra_context_tokens_proj(lowerCAmelCase__      )
								a      :		List[str]	=				clip_extra_context_tokens.reshape(lowerCAmelCase__      ,					-1      ,					self.clip_extra_context_tokens      )
								a      :		Optional[Any]	=				clip_extra_context_tokens.permute(0      ,					2      ,					1      )

								a      :		Optional[int]	=				self.encoder_hidden_states_proj(lowerCAmelCase__      )
								a      :		str	=				self.text_encoder_hidden_states_norm(lowerCAmelCase__      )
								a      :		List[str]	=				torch.cat([clip_extra_context_tokens, text_encoder_hidden_states]      ,					dim=1      )

								return text_encoder_hidden_states, additive_clip_time_embeddings


