




import torch
from torch import nn

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


class 				UpperCamelCase_    ( UpperCAmelCase__		,					UpperCAmelCase__     ):

    '''simple docstring'''






    @register_to_config
    def __init__(			self      :	int					, *,
        UpperCAmelCase__      :	int = 4					, UpperCAmelCase__      :	int = 768					, UpperCAmelCase__      :	int					, UpperCAmelCase__      :	List[str]					, )						->Any:





           '''simple docstring'''

           super().__init__()

           A__       =    nn.Parameter(torch.zeros(UpperCAmelCase__))

           # parameters for additional clip time embeddings
           A__       =    nn.Linear(UpperCAmelCase__					, UpperCAmelCase__)
           A__       =    nn.Linear(UpperCAmelCase__					, UpperCAmelCase__)

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





    def        SCREAMING_SNAKE_CASE							(			self      :	int					, *, UpperCAmelCase__      :	str					, UpperCAmelCase__      :	str					, UpperCAmelCase__      :	int					, UpperCAmelCase__      :	Optional[Any])						->Any:





           '''simple docstring'''

           if do_classifier_free_guidance:
                  # Add the classifier free guidance embeddings to the image embeddings
                  A__       =    image_embeddings.shape[0]
                  A__       =    self.learned_classifier_free_guidance_embeddings.unsqueeze(0)
                  A__       =    classifier_free_guidance_embeddings.expand(
                      UpperCAmelCase__					, -1)
                  A__       =    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__       =    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__       =    self.embedding_proj(UpperCAmelCase__)
           A__       =    self.clip_image_embeddings_project_to_time_embeddings(UpperCAmelCase__)
           A__       =    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__       =    self.clip_extra_context_tokens_proj(UpperCAmelCase__)
           A__       =    clip_extra_context_tokens.reshape(UpperCAmelCase__					, -1					, self.clip_extra_context_tokens)
           A__       =    clip_extra_context_tokens.permute(0					, 2					, 1)

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

           return text_encoder_hidden_states, additive_clip_time_embeddings








import tempfile
import unittest

from make_student import create_student_by_copying_alternating_layers

from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch


_lowerCamelCase	:		List[Any]							       =   """sshleifer/bart-tiny-random"""
_lowerCamelCase	:		List[Any]							       =   """patrickvonplaten/t5-tiny-random"""


@require_torch
class 				UpperCamelCase_    ( unittest.TestCase     ):

    '''simple docstring'''






    @cached_property
    def        SCREAMING_SNAKE_CASE							(			self      :	Union[str, Any])						->int:





           '''simple docstring'''

           return AutoConfig.from_pretrained(UpperCAmelCase__)


    def        SCREAMING_SNAKE_CASE							(			self      :	List[str])						->Any:





           '''simple docstring'''

           A__ ,							*A__       =    create_student_by_copying_alternating_layers(UpperCAmelCase__					, tempfile.mkdtemp()					, e=1					, d=1)
           self.assertEqual(student.config.num_hidden_layers					, 1)


    def        SCREAMING_SNAKE_CASE							(			self      :	int)						->Any:





           '''simple docstring'''

           A__ ,							*A__       =    create_student_by_copying_alternating_layers(UpperCAmelCase__					, tempfile.mkdtemp()					, e=1					, d=UpperCAmelCase__)


    def        SCREAMING_SNAKE_CASE							(			self      :	Union[str, Any])						->Union[str, Any]:





           '''simple docstring'''

           A__ ,							*A__       =    create_student_by_copying_alternating_layers(UpperCAmelCase__					, tempfile.mkdtemp()					, e=1					, d=UpperCAmelCase__)
           self.assertEqual(student.config.encoder_layers					, 1)
           self.assertEqual(student.config.decoder_layers					, self.teacher_config.encoder_layers)


    def        SCREAMING_SNAKE_CASE							(			self      :	Dict)						->int:





           '''simple docstring'''

           A__ ,							*A__       =    create_student_by_copying_alternating_layers(UpperCAmelCase__					, tempfile.mkdtemp()					, e=1					, d=1)
           self.assertEqual(student.config.encoder_layers					, 1)
           self.assertEqual(student.config.decoder_layers					, 1)





    def        SCREAMING_SNAKE_CASE							(			self      :	str)						->List[Any]:





           '''simple docstring'''

           with self.assertRaises(UpperCAmelCase__):
                  create_student_by_copying_alternating_layers(UpperCAmelCase__					, tempfile.mkdtemp()					, e=UpperCAmelCase__					, d=UpperCAmelCase__)


