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": 10, "max_num_jobs": 1}, [range(10							)]),
        ({"num_shards": 10, "max_num_jobs": 10}, [range(_lowercase,  i + 1							) for i in range(10							)]),
        ({"num_shards": 1, "max_num_jobs": 10}, [range(1							)]),
        ({"num_shards": 10, "max_num_jobs": 3}, [range(0,  4							), range(4,  7							), range(7,  10							)]),
        ({"num_shards": 3, "max_num_jobs": 10}, [range(0,  1							), range(1,  2							), range(2,  3							)]),
    ],  )
def    lowerCAmelCase_ (     __A,  __A							)     ->				str:







      '''simple docstring'''


      UpperCAmelCase__								=  _distribute_shards(**_lowercase							)
      assert out == expected




@pytest.mark.parametrize(
    "gen_kwargs, max_num_jobs, expected",  [
        ({"foo": 0}, 10, [{"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    lowerCAmelCase_ (     __A,  __A,  __A							)     ->				Any:







      '''simple docstring'''


      UpperCAmelCase__								=  _split_gen_kwargs(_lowercase,  _lowercase							)
      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    lowerCAmelCase_ (     __A,  __A							)     ->				List[Any]:







      '''simple docstring'''


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










"""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


