

import pytest

from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs




@pytest.mark.parametrize(
    'kwargs, expected'					,[
        ({'num_shards': 0, 'max_num_jobs': 1}, []),
        ({'num_shards': 1_0, 'max_num_jobs': 1}, [range(1_0				)]),
        ({'num_shards': 1_0, 'max_num_jobs': 1_0}, [range(_lowerCamelCase					,i + 1				) for i in range(1_0				)]),
        ({'num_shards': 1, 'max_num_jobs': 1_0}, [range(1				)]),
        ({'num_shards': 1_0, 'max_num_jobs': 3}, [range(0					,4				), range(4					,7				), range(7					,1_0				)]),
        ({'num_shards': 3, 'max_num_jobs': 1_0}, [range(0					,1				), range(1					,2				), range(2					,3				)]),
    ]					,)
def 					_lowercase     (				a_				:					Any					,a_				:					Any				)				->	Optional[int]:




					'''simple docstring'''







					__magic_name__							=    _distribute_shards(**_lowerCamelCase				)
					assert out == expected




@pytest.mark.parametrize(
    'gen_kwargs, max_num_jobs, expected'					,[
        ({'foo': 0}, 1_0, [{'foo': 0}]),
        ({'shards': [0, 1, 2, 3]}, 1, [{'shards': [0, 1, 2, 3]}]),
        ({'shards': [0, 1, 2, 3]}, 4, [{'shards': [0]}, {'shards': [1]}, {'shards': [2]}, {'shards': [3]}]),
        ({'shards': [0, 1]}, 4, [{'shards': [0]}, {'shards': [1]}]),
        ({'shards': [0, 1, 2, 3]}, 2, [{'shards': [0, 1]}, {'shards': [2, 3]}]),
    ]					,)
def 					_lowercase     (				a_				:					Optional[int]					,a_				:					Optional[Any]					,a_				:					str				)				->	List[Any]:




					'''simple docstring'''







					__magic_name__							=    _split_gen_kwargs(_lowerCamelCase					,_lowerCamelCase				)
					assert out == expected




@pytest.mark.parametrize(
    'gen_kwargs, expected'					,[
        ({'foo': 0}, 1),
        ({'shards': [0]}, 1),
        ({'shards': [0, 1, 2, 3]}, 4),
        ({'shards': [0, 1, 2, 3], 'foo': 0}, 4),
        ({'shards': [0, 1, 2, 3], 'other': (0, 1)}, 4),
        ({'shards': [0, 1, 2, 3], 'shards2': [0, 1]}, RuntimeError),
    ]					,)
def 					_lowercase     (				a_				:					Tuple					,a_				:					Dict				)				->	Optional[int]:




					'''simple docstring'''







					if expected is RuntimeError:
										with pytest.raises(_lowerCamelCase				):
															_number_of_shards_in_gen_kwargs(_lowerCamelCase				)
					else:
										__magic_name__							=    _number_of_shards_in_gen_kwargs(_lowerCamelCase				)
										assert out == expected







import torch
from torch import nn

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

class 				__UpperCamelCase				(					SCREAMING_SNAKE_CASE  ,						SCREAMING_SNAKE_CASE			):







	@register_to_config
	def __init__(   self: Optional[Any]  , *,
	    __UpperCamelCase: int = 4  , __UpperCamelCase: int = 7_68  , __UpperCamelCase: int  , __UpperCamelCase: Any  , ):

						'''simple docstring'''





						super().__init__()

						__magic_name__							=    nn.Parameter(torch.zeros(__UpperCamelCase      )      )

						# parameters for additional clip time embeddings
						__magic_name__							=    nn.Linear(__UpperCamelCase  , __UpperCamelCase      )
						__magic_name__							=    nn.Linear(__UpperCamelCase  , __UpperCamelCase      )

						# parameters for encoder hidden states
						__magic_name__							=    clip_extra_context_tokens
						__magic_name__							=    nn.Linear(
						    __UpperCamelCase  , self.clip_extra_context_tokens * cross_attention_dim      )
						__magic_name__							=    nn.Linear(__UpperCamelCase  , __UpperCamelCase      )
						__magic_name__							=    nn.LayerNorm(__UpperCamelCase      )







	def 	_SCREAMING_SNAKE_CASE							(   self: List[str]  , *, __UpperCamelCase: Union[str, Any]  , __UpperCamelCase: str  , __UpperCamelCase: Optional[Any]  , __UpperCamelCase: Any      ):

						'''simple docstring'''





						if do_classifier_free_guidance:
											# Add the classifier free guidance embeddings to the image embeddings
											__magic_name__							=    image_embeddings.shape[0]
											__magic_name__							=    self.learned_classifier_free_guidance_embeddings.unsqueeze(0      )
											__magic_name__							=    classifier_free_guidance_embeddings.expand(
											    __UpperCamelCase  , -1      )
											__magic_name__							=    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]

						__magic_name__							=    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, ...
						__magic_name__							=    self.embedding_proj(__UpperCamelCase      )
						__magic_name__							=    self.clip_image_embeddings_project_to_time_embeddings(__UpperCamelCase      )
						__magic_name__							=    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"
						__magic_name__							=    self.clip_extra_context_tokens_proj(__UpperCamelCase      )
						__magic_name__							=    clip_extra_context_tokens.reshape(__UpperCamelCase  , -1  , self.clip_extra_context_tokens      )
						__magic_name__							=    clip_extra_context_tokens.permute(0  , 2  , 1      )

						__magic_name__							=    self.encoder_hidden_states_proj(__UpperCamelCase      )
						__magic_name__							=    self.text_encoder_hidden_states_norm(__UpperCamelCase      )
						__magic_name__							=    torch.cat([clip_extra_context_tokens, text_encoder_hidden_states]  , dim=1      )

						return text_encoder_hidden_states, additive_clip_time_embeddings




