import unittest

from transformers import BertGenerationConfig, is_torch_available
from transformers.testing_utils import require_torch, 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 BertGenerationDecoder, BertGenerationEncoder



class  UpperCamelCase				:

     '''simple docstring'''

     def __init__(					self				,				A_				,				A_=13				,				A_=7				,				A_=True				,				A_=True				,				A_=99				,				A_=32				,				A_=5				,				A_=4				,				A_=37				,				A_="gelu"				,				A_=0.1				,				A_=0.1				,				A_=50				,				A_=0.02				,				A_=True				,				A_=None				,				) ->				List[str]:






           """simple docstring"""
           _lowerCamelCase			    =				parent
           _lowerCamelCase			    =				batch_size
           _lowerCamelCase			    =				seq_length
           _lowerCamelCase			    =				is_training
           _lowerCamelCase			    =				use_input_mask
           _lowerCamelCase			    =				vocab_size
           _lowerCamelCase			    =				hidden_size
           _lowerCamelCase			    =				num_hidden_layers
           _lowerCamelCase			    =				num_attention_heads
           _lowerCamelCase			    =				intermediate_size
           _lowerCamelCase			    =				hidden_act
           _lowerCamelCase			    =				hidden_dropout_prob
           _lowerCamelCase			    =				attention_probs_dropout_prob
           _lowerCamelCase			    =				max_position_embeddings
           _lowerCamelCase			    =				initializer_range
           _lowerCamelCase			    =				use_labels
           _lowerCamelCase			    =				scope



     def 		UpperCamelCase_						(					self						) ->				Optional[Any]:






           """simple docstring"""
           _lowerCamelCase			    =				ids_tensor([self.batch_size, self.seq_length]				,				self.vocab_size						)

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

           if self.use_labels:
                 _lowerCamelCase			    =				ids_tensor([self.batch_size, self.seq_length]				,				self.vocab_size						)

           _lowerCamelCase			    =				self.get_config()

           return config, input_ids, input_mask, token_labels



     def 		UpperCamelCase_						(					self						) ->				Optional[Any]:






           """simple docstring"""
           return BertGenerationConfig(
               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				,				is_decoder=A_				,				initializer_range=self.initializer_range				,				)



     def 		UpperCamelCase_						(					self						) ->				List[str]:






           """simple docstring"""
           (
               (
               _lowerCamelCase
           )    ,					(
               _lowerCamelCase
           )    ,					(
               _lowerCamelCase
           )    ,					(
               _lowerCamelCase
           )    ,					
           )			    =				self.prepare_config_and_inputs()

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

           return (
               config,
               input_ids,
               input_mask,
               token_labels,
               encoder_hidden_states,
               encoder_attention_mask,
           )



     def 		UpperCamelCase_						(					self				,				A_				,				A_				,				A_				,				A_				,				**A_				,				) ->				Optional[Any]:






           """simple docstring"""
           _lowerCamelCase			    =				BertGenerationEncoder(config=A_						)
           model.to(A_						)
           model.eval()
           _lowerCamelCase			    =				model(A_				,				attention_mask=A_						)
           _lowerCamelCase			    =				model(A_						)
           self.parent.assertEqual(result.last_hidden_state.shape				,				(self.batch_size, self.seq_length, self.hidden_size)						)



     def 		UpperCamelCase_						(					self				,				A_				,				A_				,				A_				,				A_				,				A_				,				A_				,				**A_				,				) ->				str:






           """simple docstring"""
           _lowerCamelCase			    =				True
           _lowerCamelCase			    =				BertGenerationEncoder(config=A_						)
           model.to(A_						)
           model.eval()
           _lowerCamelCase			    =				model(
               A_				,				attention_mask=A_				,				encoder_hidden_states=A_				,				encoder_attention_mask=A_				,				)
           _lowerCamelCase			    =				model(
               A_				,				attention_mask=A_				,				encoder_hidden_states=A_				,				)
           self.parent.assertEqual(result.last_hidden_state.shape				,				(self.batch_size, self.seq_length, self.hidden_size)						)



     def 		UpperCamelCase_						(					self				,				A_				,				A_				,				A_				,				A_				,				A_				,				A_				,				**A_				,				) ->				Dict:






           """simple docstring"""
           _lowerCamelCase			    =				True
           _lowerCamelCase			    =				True
           _lowerCamelCase			    =				BertGenerationDecoder(config=A_						).to(A_						).eval()

           # first forward pass
           _lowerCamelCase			    =				model(
               A_				,				attention_mask=A_				,				encoder_hidden_states=A_				,				encoder_attention_mask=A_				,				use_cache=A_				,				)
           _lowerCamelCase			    =				outputs.past_key_values

           # create hypothetical multiple next token and extent to next_input_ids
           _lowerCamelCase			    =				ids_tensor((self.batch_size, 3)				,				config.vocab_size						)
           _lowerCamelCase			    =				ids_tensor((self.batch_size, 3)				,				vocab_size=2						)

           # append to next input_ids and
           _lowerCamelCase			    =				torch.cat([input_ids, next_tokens]				,				dim=-1						)
           _lowerCamelCase			    =				torch.cat([input_mask, next_mask]				,				dim=-1						)

           _lowerCamelCase			    =				model(
               A_				,				attention_mask=A_				,				encoder_hidden_states=A_				,				encoder_attention_mask=A_				,				output_hidden_states=A_				,				)['''hidden_states'''][0]
           _lowerCamelCase			    =				model(
               A_				,				attention_mask=A_				,				encoder_hidden_states=A_				,				encoder_attention_mask=A_				,				past_key_values=A_				,				output_hidden_states=A_				,				)['''hidden_states'''][0]

           # select random slice
           _lowerCamelCase			    =				ids_tensor((1,)				,				output_from_past.shape[-1]						).item()
           _lowerCamelCase			    =				output_from_no_past[:, -3:, random_slice_idx].detach()
           _lowerCamelCase			    =				output_from_past[:, :, random_slice_idx].detach()

           self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]						)

           # test that outputs are equal for slice
           self.parent.assertTrue(torch.allclose(A_				,				A_				,				atol=1E-3						)						)



     def 		UpperCamelCase_						(					self				,				A_				,				A_				,				A_				,				A_				,				*A_				,				) ->				int:






           """simple docstring"""
           _lowerCamelCase			    =				BertGenerationDecoder(A_						)
           model.to(A_						)
           model.eval()
           _lowerCamelCase			    =				model(A_				,				attention_mask=A_				,				labels=A_						)
           self.parent.assertEqual(result.logits.shape				,				(self.batch_size, self.seq_length, self.vocab_size)						)





     def 		UpperCamelCase_						(					self						) ->				int:






           """simple docstring"""
           _lowerCamelCase    ,					_lowerCamelCase    ,					_lowerCamelCase    ,					_lowerCamelCase			    =				self.prepare_config_and_inputs()
           _lowerCamelCase			    =				{'''input_ids''': input_ids, '''attention_mask''': input_mask}
           return config, inputs_dict





@require_torch
class  UpperCamelCase				( __lowercase   ,  __lowercase   ,  __lowercase   ,  unittest.TestCase ):

     '''simple docstring'''

     A_										=  (BertGenerationEncoder, BertGenerationDecoder) if is_torch_available() else ()
     A_										=  (BertGenerationDecoder,) if is_torch_available() else ()
     A_										=  (
         {'feature-extraction': BertGenerationEncoder, 'text-generation': BertGenerationDecoder}
         if is_torch_available()
         else {}
     )



     def 		UpperCamelCase_						(					self						) ->				int:






           """simple docstring"""
           _lowerCamelCase			    =				BertGenerationEncoderTester(self						)
           _lowerCamelCase			    =				ConfigTester(self				,				config_class=A_				,				hidden_size=37						)



     def 		UpperCamelCase_						(					self						) ->				Any:






           """simple docstring"""
           self.config_tester.run_common_tests()



     def 		UpperCamelCase_						(					self						) ->				Optional[int]:






           """simple docstring"""
           _lowerCamelCase			    =				self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*A_						)



     def 		UpperCamelCase_						(					self						) ->				List[str]:






           """simple docstring"""
           _lowerCamelCase    ,					_lowerCamelCase    ,					_lowerCamelCase    ,					_lowerCamelCase			    =				self.model_tester.prepare_config_and_inputs()
           _lowerCamelCase			    =				'''bert'''
           self.model_tester.create_and_check_model(A_				,				A_				,				A_				,				A_						)



     def 		UpperCamelCase_						(					self						) ->				Optional[Any]:






           """simple docstring"""
           _lowerCamelCase			    =				self.model_tester.prepare_config_and_inputs_for_decoder()
           self.model_tester.create_and_check_model_as_decoder(*A_						)



     def 		UpperCamelCase_						(					self						) ->				int:






           """simple docstring"""
           _lowerCamelCase			    =				self.model_tester.prepare_config_and_inputs_for_decoder()
           self.model_tester.create_and_check_decoder_model_past_large_inputs(*A_						)



     def 		UpperCamelCase_						(					self						) ->				Optional[Any]:






           """simple docstring"""
           # This regression test was failing with PyTorch < 1.3
           (
               (
               _lowerCamelCase
           )    ,					(
               _lowerCamelCase
           )    ,					(
               _lowerCamelCase
           )    ,					(
               _lowerCamelCase
           )    ,					(
               _lowerCamelCase
           )    ,					(
               _lowerCamelCase
           )    ,					
           )			    =				self.model_tester.prepare_config_and_inputs_for_decoder()

           _lowerCamelCase			    =				None

           self.model_tester.create_and_check_model_as_decoder(
               A_				,				A_				,				A_				,				A_				,				A_				,				A_				,				)



     def 		UpperCamelCase_						(					self						) ->				Any:






           """simple docstring"""
           _lowerCamelCase			    =				self.model_tester.prepare_config_and_inputs_for_decoder()
           self.model_tester.create_and_check_for_causal_lm(*A_						)





     @slow
     def 		UpperCamelCase_						(					self						) ->				Optional[Any]:






           """simple docstring"""
           _lowerCamelCase			    =				BertGenerationEncoder.from_pretrained('''google/bert_for_seq_generation_L-24_bbc_encoder'''						)
           self.assertIsNotNone(A_						)





@require_torch
class  UpperCamelCase				( unittest.TestCase ):

     '''simple docstring'''

     @slow
     def 		UpperCamelCase_						(					self						) ->				Union[str, Any]:






           """simple docstring"""
           _lowerCamelCase			    =				BertGenerationEncoder.from_pretrained('''google/bert_for_seq_generation_L-24_bbc_encoder'''						)
           _lowerCamelCase			    =				torch.tensor([[1_01, 75_92, 10_10, 20_26, 38_99, 20_03, 1_01_40, 1_02]]						)
           with torch.no_grad():
                 _lowerCamelCase			    =				model(A_						)[0]
           _lowerCamelCase			    =				torch.Size([1, 8, 10_24]						)
           self.assertEqual(output.shape				,				A_						)
           _lowerCamelCase			    =				torch.tensor(
               [[[0.1775, 0.0083, -0.0321], [1.6002, 0.1287, 0.3912], [2.1473, 0.5791, 0.6066]]]						)
           self.assertTrue(torch.allclose(output[:, :3, :3]				,				A_				,				atol=1E-4						)						)





@require_torch
class  UpperCamelCase				( unittest.TestCase ):

     '''simple docstring'''

     @slow
     def 		UpperCamelCase_						(					self						) ->				int:






           """simple docstring"""
           _lowerCamelCase			    =				BertGenerationDecoder.from_pretrained('''google/bert_for_seq_generation_L-24_bbc_encoder'''						)
           _lowerCamelCase			    =				torch.tensor([[1_01, 75_92, 10_10, 20_26, 38_99, 20_03, 1_01_40, 1_02]]						)
           with torch.no_grad():
                 _lowerCamelCase			    =				model(A_						)[0]
           _lowerCamelCase			    =				torch.Size([1, 8, 5_03_58]						)
           self.assertEqual(output.shape				,				A_						)
           _lowerCamelCase			    =				torch.tensor(
               [[[-0.5788, -2.5994, -3.7054], [0.0438, 4.7997, 1.8795], [1.5862, 6.6409, 4.4638]]]						)
           self.assertTrue(torch.allclose(output[:, :3, :3]				,				A_				,				atol=1E-4						)						)
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .unet_ad_blocks import get_down_block, get_mid_block, get_out_block, get_up_block



@dataclass
class  UpperCamelCase				( __lowercase ):

     '''simple docstring'''

     A_										=  42





class  UpperCamelCase				( __lowercase   ,  __lowercase ):

     '''simple docstring'''

     @register_to_config
     def __init__(					self				,				A_ = 6_55_36				,				A_ = None				,				A_ = 2				,				A_ = 2				,				A_ = 0				,				A_ = "fourier"				,				A_ = True				,				A_ = False				,				A_ = 0.0				,				A_ = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")				,				A_ = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")				,				A_ = "UNetMidBlock1D"				,				A_ = None				,				A_ = (32, 32, 64)				,				A_ = None				,				A_ = 8				,				A_ = 1				,				A_ = False				,				) ->				Dict:






           """simple docstring"""
           super().__init__()
           _lowerCamelCase			    =				sample_size

           # time
           if time_embedding_type == "fourier":
                 _lowerCamelCase			    =				GaussianFourierProjection(
                     embedding_size=8				,				set_W_to_weight=A_				,				log=A_				,				flip_sin_to_cos=A_						)
                 _lowerCamelCase			    =				2 * block_out_channels[0]
           elif time_embedding_type == "positional":
                 _lowerCamelCase			    =				Timesteps(
                     block_out_channels[0]				,				flip_sin_to_cos=A_				,				downscale_freq_shift=A_						)
                 _lowerCamelCase			    =				block_out_channels[0]

           if use_timestep_embedding:
                 _lowerCamelCase			    =				block_out_channels[0] * 4
                 _lowerCamelCase			    =				TimestepEmbedding(
                     in_channels=A_				,				time_embed_dim=A_				,				act_fn=A_				,				out_dim=block_out_channels[0]				,				)

           _lowerCamelCase			    =				nn.ModuleList([]						)
           _lowerCamelCase			    =				None
           _lowerCamelCase			    =				nn.ModuleList([]						)
           _lowerCamelCase			    =				None

           # down
           _lowerCamelCase			    =				in_channels
           for i, down_block_type in enumerate(A_						):
                 _lowerCamelCase			    =				output_channel
                 _lowerCamelCase			    =				block_out_channels[i]

                 if i == 0:
                       input_channel += extra_in_channels

                 _lowerCamelCase			    =				i == len(A_						) - 1

                 _lowerCamelCase			    =				get_down_block(
                     A_				,				num_layers=A_				,				in_channels=A_				,				out_channels=A_				,				temb_channels=block_out_channels[0]				,				add_downsample=not is_final_block or downsample_each_block				,				)
                 self.down_blocks.append(A_						)

           # mid
           _lowerCamelCase			    =				get_mid_block(
               A_				,				in_channels=block_out_channels[-1]				,				mid_channels=block_out_channels[-1]				,				out_channels=block_out_channels[-1]				,				embed_dim=block_out_channels[0]				,				num_layers=A_				,				add_downsample=A_				,				)

           # up
           _lowerCamelCase			    =				list(reversed(A_						)						)
           _lowerCamelCase			    =				reversed_block_out_channels[0]
           if out_block_type is None:
                 _lowerCamelCase			    =				out_channels
           else:
                 _lowerCamelCase			    =				block_out_channels[0]

           for i, up_block_type in enumerate(A_						):
                 _lowerCamelCase			    =				output_channel
                 _lowerCamelCase			    =				(
                     reversed_block_out_channels[i + 1] if i < len(A_						) - 1 else final_upsample_channels
                 )

                 _lowerCamelCase			    =				i == len(A_						) - 1

                 _lowerCamelCase			    =				get_up_block(
                     A_				,				num_layers=A_				,				in_channels=A_				,				out_channels=A_				,				temb_channels=block_out_channels[0]				,				add_upsample=not is_final_block				,				)
                 self.up_blocks.append(A_						)
                 _lowerCamelCase			    =				output_channel

           # out
           _lowerCamelCase			    =				norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4				,				32						)
           _lowerCamelCase			    =				get_out_block(
               out_block_type=A_				,				num_groups_out=A_				,				embed_dim=block_out_channels[0]				,				out_channels=A_				,				act_fn=A_				,				fc_dim=block_out_channels[-1] // 4				,				)





     def 		UpperCamelCase_						(					self				,				A_				,				A_				,				A_ = True				,				) ->				Union[UNetaDOutput, Tuple]:






           """simple docstring"""
           _lowerCamelCase			    =				timestep
           if not torch.is_tensor(A_						):
                 _lowerCamelCase			    =				torch.tensor([timesteps]				,				dtype=torch.long				,				device=sample.device						)
           elif torch.is_tensor(A_						) and len(timesteps.shape						) == 0:
                 _lowerCamelCase			    =				timesteps[None].to(sample.device						)

           _lowerCamelCase			    =				self.time_proj(A_						)
           if self.config.use_timestep_embedding:
                 _lowerCamelCase			    =				self.time_mlp(A_						)
           else:
                 _lowerCamelCase			    =				timestep_embed[..., None]
                 _lowerCamelCase			    =				timestep_embed.repeat([1, 1, sample.shape[2]]						).to(sample.dtype						)
                 _lowerCamelCase			    =				timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:])						)

           # 2. down
           _lowerCamelCase			    =				()
           for downsample_block in self.down_blocks:
                 _lowerCamelCase    ,					_lowerCamelCase			    =				downsample_block(hidden_states=A_				,				temb=A_						)
                 down_block_res_samples += res_samples

           # 3. mid
           if self.mid_block:
                 _lowerCamelCase			    =				self.mid_block(A_				,				A_						)

           # 4. up
           for i, upsample_block in enumerate(self.up_blocks						):
                 _lowerCamelCase			    =				down_block_res_samples[-1:]
                 _lowerCamelCase			    =				down_block_res_samples[:-1]
                 _lowerCamelCase			    =				upsample_block(A_				,				res_hidden_states_tuple=A_				,				temb=A_						)

           # 5. post-process
           if self.out_block:
                 _lowerCamelCase			    =				self.out_block(A_				,				A_						)

           if not return_dict:
                 return (sample,)

           return UNetaDOutput(sample=A_						)