

from timeit import timeit

_lowerCAmelCase  :      Dict			 ={
    """MALAYALAM""": True,
    """String""": False,
    """rotor""": True,
    """level""": True,
    """A""": True,
    """BB""": True,
    """ABC""": False,
    """amanaplanacanalpanama""": True,  # "a man a plan a canal panama"
}
# Ensure our test data is valid
assert all((key == key[::-1]) is value for key, value in test_data.items())



def 			_A	(      SCREAMING_SNAKE_CASE      ):
	UpperCAmelCase__:	List[str]									= 0
	UpperCAmelCase__:	Tuple									= len(SCREAMING_SNAKE_CASE      ) - 1
	while start_i < end_i:
		if s[start_i] == s[end_i]:
			start_i += 1
			end_i -= 1
		else:
			return False
	return True



def 			_A	(      SCREAMING_SNAKE_CASE      ):
	UpperCAmelCase__:	Union[str, Any]									= len(SCREAMING_SNAKE_CASE      ) // 2
	UpperCAmelCase__:	List[str]									= len(SCREAMING_SNAKE_CASE      )

	# We need to traverse till half of the length of string
	# as we can get access of the i'th last element from
	# i'th index.
	# eg: [0,1,2,3,4,5] => 4th index can be accessed
	# with the help of 1st index (i==n-i-1)
	# where n is length of string
	return all(s[i] == s[n - i - 1] for i in range(SCREAMING_SNAKE_CASE      )      )



def 			_A	(      SCREAMING_SNAKE_CASE      ):
	if len(SCREAMING_SNAKE_CASE      ) <= 2:
		return True
	if s[0] == s[len(SCREAMING_SNAKE_CASE      ) - 1]:
		return is_palindrome_recursive(s[1:-1]      )
	else:
		return False



def 			_A	(      SCREAMING_SNAKE_CASE      ):
	return s == s[::-1]



def 			_A	(      SCREAMING_SNAKE_CASE      ):
	UpperCAmelCase__:	List[str]									= f"all({name}(key) is value for key, value in test_data.items())"
	UpperCAmelCase__:	Dict									= f"from __main__ import test_data, {name}"
	UpperCAmelCase__:	Optional[int]									= 5_0_0_0_0_0
	UpperCAmelCase__:	Union[str, Any]									= timeit(stmt=SCREAMING_SNAKE_CASE	,setup=SCREAMING_SNAKE_CASE	,number=SCREAMING_SNAKE_CASE      )
	print(f"{name:<35} finished {number:,} runs in {result:.5f} seconds"      )


if __name__ == "__main__":
					for key, value in test_data.items():
										assert is_palindrome(key) is is_palindrome_recursive(key)
										assert is_palindrome(key) is is_palindrome_slice(key)
										print(F"{key:21} {value}")
					print("""a man a plan a canal panama""")

					# finished 500,000 runs in 0.46793 seconds
					benchmark_function("""is_palindrome_slice""")
					# finished 500,000 runs in 0.85234 seconds
					benchmark_function("""is_palindrome""")
					# finished 500,000 runs in 1.32028 seconds
					benchmark_function("""is_palindrome_recursive""")
					# finished 500,000 runs in 2.08679 seconds
					benchmark_function("""is_palindrome_traversal""")


import torch
from torch import nn

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

class __UpperCamelCase     (				_a					,_a		):






					'''simple docstring'''



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

						UpperCAmelCase__:	int									= nn.Parameter(torch.zeros(lowerCamelCase__						)						)

						# parameters for additional clip time embeddings
						UpperCAmelCase__:	Optional[Any]									= nn.Linear(lowerCamelCase__				,      lowerCamelCase__						)
						UpperCAmelCase__:	str									= nn.Linear(lowerCamelCase__				,      lowerCamelCase__						)

						# parameters for encoder hidden states
						UpperCAmelCase__:	Tuple									= clip_extra_context_tokens
						UpperCAmelCase__:	List[str]									= nn.Linear(
						    lowerCamelCase__				,      self.clip_extra_context_tokens * cross_attention_dim						)
						UpperCAmelCase__:	Union[str, Any]									= nn.Linear(lowerCamelCase__				,      lowerCamelCase__						)
						UpperCAmelCase__:	str									= nn.LayerNorm(lowerCamelCase__						)





					def    _UpperCAmelCase    (				self				,      *, lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__						):
						if do_classifier_free_guidance:
							# Add the classifier free guidance embeddings to the image embeddings
							UpperCAmelCase__:	Any									= image_embeddings.shape[0]
							UpperCAmelCase__:	Optional[Any]									= self.learned_classifier_free_guidance_embeddings.unsqueeze(0						)
							UpperCAmelCase__:	Union[str, Any]									= classifier_free_guidance_embeddings.expand(
							    lowerCamelCase__				,      -1						)
							UpperCAmelCase__:	Tuple									= 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__:	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, ...
						UpperCAmelCase__:	List[str]									= self.embedding_proj(lowerCamelCase__						)
						UpperCAmelCase__:	Optional[int]									= self.clip_image_embeddings_project_to_time_embeddings(lowerCamelCase__						)
						UpperCAmelCase__:	Any									= 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__:	Dict									= self.clip_extra_context_tokens_proj(lowerCamelCase__						)
						UpperCAmelCase__:	Optional[Any]									= clip_extra_context_tokens.reshape(lowerCamelCase__				,      -1				,      self.clip_extra_context_tokens						)
						UpperCAmelCase__:	Dict									= clip_extra_context_tokens.permute(0				,      2				,      1						)

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

						return text_encoder_hidden_states, additive_clip_time_embeddings