





'''simple docstring'''




import torch
from torch import nn

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






class UpperCAmelCase							(		UpperCAmelCase_					,			UpperCAmelCase_							):



    @register_to_config
    def __init__(  self     ,			*,
        __A = 4     ,			__A = 768     ,			__A     ,			__A     ,			):
     super().__init__()

     __UpperCAmelCase					=   nn.Parameter(torch.zeros(__A					)					)

     # parameters for additional clip time embeddings
     __UpperCAmelCase					=   nn.Linear(__A     ,			__A					)
     __UpperCAmelCase					=   nn.Linear(__A     ,			__A					)

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




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

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

     return text_encoder_hidden_states, additive_clip_time_embeddings












'''simple docstring'''




import os
import tempfile
import unittest

from transformers import NezhaConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import (
        MODEL_FOR_PRETRAINING_MAPPING,
        NezhaForMaskedLM,
        NezhaForMultipleChoice,
        NezhaForNextSentencePrediction,
        NezhaForPreTraining,
        NezhaForQuestionAnswering,
        NezhaForSequenceClassification,
        NezhaForTokenClassification,
        NezhaModel,
    )
    from transformers.models.nezha.modeling_nezha import NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST






class UpperCAmelCase							:



    def __init__(  self     ,			__A     ,			__A=13     ,			__A=7     ,			__A=True     ,			__A=True     ,			__A=True     ,			__A=True     ,			__A=99     ,			__A=32     ,			__A=5     ,			__A=4     ,			__A=37     ,			__A="gelu"     ,			__A=0.1     ,			__A=0.1     ,			__A=128     ,			__A=32     ,			__A=16     ,			__A=2     ,			__A=0.0_2     ,			__A=3     ,			__A=4     ,			__A=None     ,			):
     __UpperCAmelCase					=   parent
     __UpperCAmelCase					=   batch_size
     __UpperCAmelCase					=   seq_length
     __UpperCAmelCase					=   is_training
     __UpperCAmelCase					=   use_input_mask
     __UpperCAmelCase					=   use_token_type_ids
     __UpperCAmelCase					=   use_labels
     __UpperCAmelCase					=   vocab_size
     __UpperCAmelCase					=   hidden_size
     __UpperCAmelCase					=   num_hidden_layers
     __UpperCAmelCase					=   num_attention_heads
     __UpperCAmelCase					=   intermediate_size
     __UpperCAmelCase					=   hidden_act
     __UpperCAmelCase					=   hidden_dropout_prob
     __UpperCAmelCase					=   attention_probs_dropout_prob
     __UpperCAmelCase					=   max_position_embeddings
     __UpperCAmelCase					=   type_vocab_size
     __UpperCAmelCase					=   type_sequence_label_size
     __UpperCAmelCase					=   initializer_range
     __UpperCAmelCase					=   num_labels
     __UpperCAmelCase					=   num_choices
     __UpperCAmelCase					=   scope



    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   ids_tensor([self.batch_size, self.seq_length]     ,			self.vocab_size					)

     __UpperCAmelCase					=   None
     if self.use_input_mask:
      __UpperCAmelCase					=   random_attention_mask([self.batch_size, self.seq_length]					)

     __UpperCAmelCase					=   None
     if self.use_token_type_ids:
      __UpperCAmelCase					=   ids_tensor([self.batch_size, self.seq_length]     ,			self.type_vocab_size					)

     __UpperCAmelCase					=   None
     __UpperCAmelCase					=   None
     __UpperCAmelCase					=   None
     if self.use_labels:
      __UpperCAmelCase					=   ids_tensor([self.batch_size]     ,			self.type_sequence_label_size					)
      __UpperCAmelCase					=   ids_tensor([self.batch_size, self.seq_length]     ,			self.num_labels					)
      __UpperCAmelCase					=   ids_tensor([self.batch_size]     ,			self.num_choices					)

     __UpperCAmelCase					=   self.get_config()

     return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels



    def 					__lowerCamelCase						(  self					):
     return NezhaConfig(
         vocab_size=self.vocab_size     ,			hidden_size=self.hidden_size     ,			num_hidden_layers=self.num_hidden_layers     ,			num_attention_heads=self.num_attention_heads     ,			intermediate_size=self.intermediate_size     ,			hidden_act=self.hidden_act     ,			hidden_dropout_prob=self.hidden_dropout_prob     ,			attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,			max_position_embeddings=self.max_position_embeddings     ,			type_vocab_size=self.type_vocab_size     ,			is_decoder=__A     ,			initializer_range=self.initializer_range     ,			)



    def 					__lowerCamelCase						(  self					):
     (
         (
         __UpperCAmelCase
     )					,    (
         __UpperCAmelCase
     )					,    (
         __UpperCAmelCase
     )					,    (
         __UpperCAmelCase
     )					,    (
         __UpperCAmelCase
     )					,    (
         __UpperCAmelCase
     )					,    (
         __UpperCAmelCase
     )					,    
     )					=   self.prepare_config_and_inputs()

     __UpperCAmelCase					=   True
     __UpperCAmelCase					=   floats_tensor([self.batch_size, self.seq_length, self.hidden_size]					)
     __UpperCAmelCase					=   ids_tensor([self.batch_size, self.seq_length]     ,			vocab_size=2					)

     return (
         config,
         input_ids,
         token_type_ids,
         input_mask,
         sequence_labels,
         token_labels,
         choice_labels,
         encoder_hidden_states,
         encoder_attention_mask,
     )



    def 					__lowerCamelCase						(  self     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A					):
     __UpperCAmelCase					=   NezhaModel(config=__A					)
     model.to(__A					)
     model.eval()
     __UpperCAmelCase					=   model(__A     ,			attention_mask=__A     ,			token_type_ids=__A					)
     __UpperCAmelCase					=   model(__A     ,			token_type_ids=__A					)
     __UpperCAmelCase					=   model(__A					)
     self.parent.assertEqual(result.last_hidden_state.shape     ,			(self.batch_size, self.seq_length, self.hidden_size)					)
     self.parent.assertEqual(result.pooler_output.shape     ,			(self.batch_size, self.hidden_size)					)



    def 					__lowerCamelCase						(  self     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			):
     __UpperCAmelCase					=   True
     __UpperCAmelCase					=   NezhaModel(__A					)
     model.to(__A					)
     model.eval()
     __UpperCAmelCase					=   model(
         __A     ,			attention_mask=__A     ,			token_type_ids=__A     ,			encoder_hidden_states=__A     ,			encoder_attention_mask=__A     ,			)
     __UpperCAmelCase					=   model(
         __A     ,			attention_mask=__A     ,			token_type_ids=__A     ,			encoder_hidden_states=__A     ,			)
     __UpperCAmelCase					=   model(__A     ,			attention_mask=__A     ,			token_type_ids=__A					)
     self.parent.assertEqual(result.last_hidden_state.shape     ,			(self.batch_size, self.seq_length, self.hidden_size)					)
     self.parent.assertEqual(result.pooler_output.shape     ,			(self.batch_size, self.hidden_size)					)



    def 					__lowerCamelCase						(  self     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A					):
     __UpperCAmelCase					=   NezhaForMaskedLM(config=__A					)
     model.to(__A					)
     model.eval()
     __UpperCAmelCase					=   model(__A     ,			attention_mask=__A     ,			token_type_ids=__A     ,			labels=__A					)
     self.parent.assertEqual(result.logits.shape     ,			(self.batch_size, self.seq_length, self.vocab_size)					)



    def 					__lowerCamelCase						(  self     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A					):
     __UpperCAmelCase					=   NezhaForNextSentencePrediction(config=__A					)
     model.to(__A					)
     model.eval()
     __UpperCAmelCase					=   model(
         __A     ,			attention_mask=__A     ,			token_type_ids=__A     ,			labels=__A     ,			)
     self.parent.assertEqual(result.logits.shape     ,			(self.batch_size, 2)					)



    def 					__lowerCamelCase						(  self     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A					):
     __UpperCAmelCase					=   NezhaForPreTraining(config=__A					)
     model.to(__A					)
     model.eval()
     __UpperCAmelCase					=   model(
         __A     ,			attention_mask=__A     ,			token_type_ids=__A     ,			labels=__A     ,			next_sentence_label=__A     ,			)
     self.parent.assertEqual(result.prediction_logits.shape     ,			(self.batch_size, self.seq_length, self.vocab_size)					)
     self.parent.assertEqual(result.seq_relationship_logits.shape     ,			(self.batch_size, 2)					)



    def 					__lowerCamelCase						(  self     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A					):
     __UpperCAmelCase					=   NezhaForQuestionAnswering(config=__A					)
     model.to(__A					)
     model.eval()
     __UpperCAmelCase					=   model(
         __A     ,			attention_mask=__A     ,			token_type_ids=__A     ,			start_positions=__A     ,			end_positions=__A     ,			)
     self.parent.assertEqual(result.start_logits.shape     ,			(self.batch_size, self.seq_length)					)
     self.parent.assertEqual(result.end_logits.shape     ,			(self.batch_size, self.seq_length)					)



    def 					__lowerCamelCase						(  self     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A					):
     __UpperCAmelCase					=   self.num_labels
     __UpperCAmelCase					=   NezhaForSequenceClassification(__A					)
     model.to(__A					)
     model.eval()
     __UpperCAmelCase					=   model(__A     ,			attention_mask=__A     ,			token_type_ids=__A     ,			labels=__A					)
     self.parent.assertEqual(result.logits.shape     ,			(self.batch_size, self.num_labels)					)



    def 					__lowerCamelCase						(  self     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A					):
     __UpperCAmelCase					=   self.num_labels
     __UpperCAmelCase					=   NezhaForTokenClassification(config=__A					)
     model.to(__A					)
     model.eval()
     __UpperCAmelCase					=   model(__A     ,			attention_mask=__A     ,			token_type_ids=__A     ,			labels=__A					)
     self.parent.assertEqual(result.logits.shape     ,			(self.batch_size, self.seq_length, self.num_labels)					)



    def 					__lowerCamelCase						(  self     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A					):
     __UpperCAmelCase					=   self.num_choices
     __UpperCAmelCase					=   NezhaForMultipleChoice(config=__A					)
     model.to(__A					)
     model.eval()
     __UpperCAmelCase					=   input_ids.unsqueeze(1					).expand(-1     ,			self.num_choices     ,			-1					).contiguous()
     __UpperCAmelCase					=   token_type_ids.unsqueeze(1					).expand(-1     ,			self.num_choices     ,			-1					).contiguous()
     __UpperCAmelCase					=   input_mask.unsqueeze(1					).expand(-1     ,			self.num_choices     ,			-1					).contiguous()
     __UpperCAmelCase					=   model(
         __A     ,			attention_mask=__A     ,			token_type_ids=__A     ,			labels=__A     ,			)
     self.parent.assertEqual(result.logits.shape     ,			(self.batch_size, self.num_choices)					)




    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   self.prepare_config_and_inputs()
     (
         (
         __UpperCAmelCase
     )					,    (
         __UpperCAmelCase
     )					,    (
         __UpperCAmelCase
     )					,    (
         __UpperCAmelCase
     )					,    (
         __UpperCAmelCase
     )					,    (
         __UpperCAmelCase
     )					,    (
         __UpperCAmelCase
     )					,    
     )					=   config_and_inputs
     __UpperCAmelCase					=   {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
     return config, inputs_dict






@require_torch
class UpperCAmelCase							(		UpperCAmelCase_					,			UpperCAmelCase_					,			UpperCAmelCase_					,			unittest.TestCase							):
    _A :			List[str]						   =       (
        (
            NezhaModel,
            NezhaForMaskedLM,
            NezhaForMultipleChoice,
            NezhaForNextSentencePrediction,
            NezhaForPreTraining,
            NezhaForQuestionAnswering,
            NezhaForSequenceClassification,
            NezhaForTokenClassification,
        )
        if is_torch_available()
        else ()
    )
    _A :			str						   =       (
        {
            """feature-extraction""": NezhaModel,
            """fill-mask""": NezhaForMaskedLM,
            """question-answering""": NezhaForQuestionAnswering,
            """text-classification""": NezhaForSequenceClassification,
            """token-classification""": NezhaForTokenClassification,
            """zero-shot""": NezhaForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )
    _A :			Dict						   =       True



    def 					__lowerCamelCase						(  self     ,			__A     ,			__A     ,			__A=False					):
     __UpperCAmelCase					=   super()._prepare_for_class(__A     ,			__A     ,			return_labels=__A					)

     if return_labels:
      if model_class in get_values(__A					):
       __UpperCAmelCase					=   torch.zeros(
           (self.model_tester.batch_size, self.model_tester.seq_length)     ,			dtype=torch.long     ,			device=__A					)
       __UpperCAmelCase					=   torch.zeros(
           self.model_tester.batch_size     ,			dtype=torch.long     ,			device=__A					)
     return inputs_dict



    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   NezhaModelTester(self					)
     __UpperCAmelCase					=   ConfigTester(self     ,			config_class=__A     ,			hidden_size=37					)



    def 					__lowerCamelCase						(  self					):
     self.config_tester.run_common_tests()



    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*__A					)



    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   self.model_tester.prepare_config_and_inputs_for_decoder()
     self.model_tester.create_and_check_model_as_decoder(*__A					)



    def 					__lowerCamelCase						(  self					):
     # This regression test was failing with PyTorch < 1.3
     (
         (
         __UpperCAmelCase
     )					,    (
         __UpperCAmelCase
     )					,    (
         __UpperCAmelCase
     )					,    (
         __UpperCAmelCase
     )					,    (
         __UpperCAmelCase
     )					,    (
         __UpperCAmelCase
     )					,    (
         __UpperCAmelCase
     )					,    (
         __UpperCAmelCase
     )					,    (
         __UpperCAmelCase
     )					,    
     )					=   self.model_tester.prepare_config_and_inputs_for_decoder()

     __UpperCAmelCase					=   None

     self.model_tester.create_and_check_model_as_decoder(
         __A     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			)



    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_masked_lm(*__A					)



    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_multiple_choice(*__A					)



    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_next_sequence_prediction(*__A					)



    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_pretraining(*__A					)



    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_question_answering(*__A					)



    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_sequence_classification(*__A					)



    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_token_classification(*__A					)



    @slow
    def 					__lowerCamelCase						(  self					):
     for model_name in NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
      __UpperCAmelCase					=   NezhaModel.from_pretrained(__A					)
      self.assertIsNotNone(__A					)




    @slow
    @require_torch_gpu
    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					,    __UpperCAmelCase					=   self.model_tester.prepare_config_and_inputs_for_common()
     for model_class in self.all_model_classes:
      # NezhaForMultipleChoice behaves incorrectly in JIT environments.
      if model_class == NezhaForMultipleChoice:
       return

      __UpperCAmelCase					=   True
      __UpperCAmelCase					=   model_class(config=__A					)

      __UpperCAmelCase					=   self._prepare_for_class(__A     ,			__A					)
      __UpperCAmelCase					=   torch.jit.trace(
          __A     ,			(inputs_dict['input_ids'].to('cpu'					), inputs_dict['attention_mask'].to('cpu'					))					)

      with tempfile.TemporaryDirectory() as tmp:
       torch.jit.save(__A     ,			os.path.join(__A     ,			'bert.pt'					)					)
       __UpperCAmelCase					=   torch.jit.load(os.path.join(__A     ,			'bert.pt'					)     ,			map_location=__A					)
       loaded(inputs_dict['input_ids'].to(__A					)     ,			inputs_dict['attention_mask'].to(__A					)					)






@require_torch
class UpperCAmelCase							(		unittest.TestCase							):



    @slow
    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   NezhaModel.from_pretrained('sijunhe/nezha-cn-base'					)
     __UpperCAmelCase					=   torch.tensor([[0, 1, 2, 3, 4, 5]]					)
     __UpperCAmelCase					=   torch.tensor([[0, 1, 1, 1, 1, 1]]					)
     with torch.no_grad():
      __UpperCAmelCase					=   model(__A     ,			attention_mask=__A					)[0]
     __UpperCAmelCase					=   torch.Size((1, 6, 768)					)
     self.assertEqual(output.shape     ,			__A					)
     __UpperCAmelCase					=   torch.tensor([[[0.0_6_8_5, 0.2_4_4_1, 0.1_1_0_2], [0.0_6_0_0, 0.1_9_0_6, 0.1_3_4_9], [0.0_2_2_1, 0.0_8_1_9, 0.0_5_8_6]]]					)

     self.assertTrue(torch.allclose(output[:, 1:4, 1:4]     ,			__A     ,			atol=1E-4					)					)




    @slow
    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   NezhaForMaskedLM.from_pretrained('sijunhe/nezha-cn-base'					)
     __UpperCAmelCase					=   torch.tensor([[0, 1, 2, 3, 4, 5]]					)
     __UpperCAmelCase					=   torch.tensor([[1, 1, 1, 1, 1, 1]]					)
     with torch.no_grad():
      __UpperCAmelCase					=   model(__A     ,			attention_mask=__A					)[0]
     __UpperCAmelCase					=   torch.Size((1, 6, 21_128)					)
     self.assertEqual(output.shape     ,			__A					)
     __UpperCAmelCase					=   torch.tensor(
         [[-2.7_9_3_9, -1.7_9_0_2, -2.2_1_8_9], [-2.8_5_8_5, -1.8_9_0_8, -2.3_7_2_3], [-2.6_4_9_9, -1.7_7_5_0, -2.2_5_5_8]]					)

     self.assertTrue(torch.allclose(output[:, 1:4, 1:4]     ,			__A     ,			atol=1E-4					)					)





