
"""simple docstring"""



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       lowerCAmelCase__				(						__SCREAMING_SNAKE_CASE	):
     __a						=					42






class       lowerCAmelCase__				(						__SCREAMING_SNAKE_CASE ,	__SCREAMING_SNAKE_CASE	):







     @register_to_config
     def __init__(							self			:			Union[str, Any]    ,      _lowerCamelCase			:			Union[str, Any] = 65536    ,      _lowerCamelCase			:			List[str] = None    ,      _lowerCamelCase			:			str = 2    ,      _lowerCamelCase			:			Tuple = 2    ,      _lowerCamelCase			:			Optional[int] = 0    ,      _lowerCamelCase			:			Dict = "fourier"    ,      _lowerCamelCase			:			Tuple = True    ,      _lowerCamelCase			:			int = False    ,      _lowerCamelCase			:			List[str] = 0.0    ,      _lowerCamelCase			:			List[Any] = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")    ,      _lowerCamelCase			:			str = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")    ,      _lowerCamelCase			:			List[Any] = "UNetMidBlock1D"    ,      _lowerCamelCase			:			Optional[int] = None    ,      _lowerCamelCase			:			List[Any] = (32, 32, 64)    ,      _lowerCamelCase			:			Union[str, Any] = None    ,      _lowerCamelCase			:			List[Any] = 8    ,      _lowerCamelCase			:			str = 1    ,      _lowerCamelCase			:			str = False    ,      ):
       super().__init__()
       _snake_case					     =       sample_size

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

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

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

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

         if i == 0:
           input_channel += extra_in_channels

         _snake_case					     =       i == len(_a			) - 1

         _snake_case					     =       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
       _snake_case					     =       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
       _snake_case					     =       list(reversed(_a			)			)
       _snake_case					     =       reversed_block_out_channels[0]
       if out_block_type is None:
         _snake_case					     =       out_channels
       else:
         _snake_case					     =       block_out_channels[0]

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

         _snake_case					     =       i == len(_a			) - 1

         _snake_case					     =       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			)
         _snake_case					     =       output_channel

       # out
       _snake_case					     =       norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4    ,      32			)
       _snake_case					     =       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      lowercase	(							self			:			Dict    ,      _lowerCamelCase			:			int    ,      _lowerCamelCase			:			Any    ,      _lowerCamelCase			:			List[str] = True    ,      ):
       _snake_case					     =       timestep
       if not torch.is_tensor(_a			):
         _snake_case					     =       torch.tensor([timesteps]    ,      dtype=torch.long    ,      device=sample.device			)
       elif torch.is_tensor(_a			) and len(timesteps.shape			) == 0:
         _snake_case					     =       timesteps[None].to(sample.device			)

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

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

       # 3. mid
       if self.mid_block:
         _snake_case					     =       self.mid_block(_a    ,      _a			)

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

       # 5. post-process
       if self.out_block:
         _snake_case					     =       self.out_block(_a    ,      _a			)

       if not return_dict:
         return (sample,)

       return UNetaDOutput(sample=_a			)







"""simple docstring"""




import gc
import unittest

from transformers import CTRLConfig, 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, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch

				from transformers import (
				    CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
				    CTRLForSequenceClassification,
				    CTRLLMHeadModel,
				    CTRLModel,
				)
class 					__lowerCAmelCase					:


		'''simple docstring'''



		def __init__(  self							, _a							, _a=14							, _a=7							, _a=True							, _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=512							, _a=16							, _a=2							, _a=0.02							, _a=3							, _a=4							, _a=None							, ):
				__a           =			parent
				__a           =			batch_size
				__a           =			seq_length
				__a           =			is_training
				__a           =			use_token_type_ids
				__a           =			use_input_mask
				__a           =			use_labels
				__a           =			use_mc_token_ids
				__a           =			vocab_size
				__a           =			hidden_size
				__a           =			num_hidden_layers
				__a           =			num_attention_heads
				__a           =			intermediate_size
				__a           =			hidden_act
				__a           =			hidden_dropout_prob
				__a           =			attention_probs_dropout_prob
				__a           =			max_position_embeddings
				__a           =			type_vocab_size
				__a           =			type_sequence_label_size
				__a           =			initializer_range
				__a           =			num_labels
				__a           =			num_choices
				__a           =			scope
				__a           =			self.vocab_size - 1

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

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

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

				__a           =			None
				if self.use_mc_token_ids:
						__a           =			ids_tensor([self.batch_size, self.num_choices]							, self.seq_length      )

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

				__a           =			self.get_config()

				__a           =			ids_tensor([self.num_hidden_layers, self.num_attention_heads]							, 2      )

				return (
				    config,
				    input_ids,
				    input_mask,
				    head_mask,
				    token_type_ids,
				    mc_token_ids,
				    sequence_labels,
				    token_labels,
				    choice_labels,
				)

		def 			__UpperCAmelCase       (  self      ):
				return CTRLConfig(
				    vocab_size=self.vocab_size							, n_embd=self.hidden_size							, n_layer=self.num_hidden_layers							, n_head=self.num_attention_heads							, n_positions=self.max_position_embeddings							, pad_token_id=self.pad_token_id							, )

		def 			__UpperCAmelCase       (  self							, _a							, _a							, _a							, _a							, _a							, *_a      ):
				__a           =			CTRLModel(config=_a      )
				model.to(_a      )
				model.eval()

				model(_a							, token_type_ids=_a							, head_mask=_a      )
				model(_a							, token_type_ids=_a      )
				__a           =			model(_a      )
				self.parent.assertEqual(result.last_hidden_state.shape							, (self.batch_size, self.seq_length, self.hidden_size)      )
				self.parent.assertEqual(len(result.past_key_values      )							, config.n_layer      )

		def 			__UpperCAmelCase       (  self							, _a							, _a							, _a							, _a							, _a							, *_a      ):
				__a           =			CTRLLMHeadModel(_a      )
				model.to(_a      )
				model.eval()

				__a           =			model(_a							, token_type_ids=_a							, labels=_a      )
				self.parent.assertEqual(result.loss.shape							, ()      )
				self.parent.assertEqual(result.logits.shape							, (self.batch_size, self.seq_length, self.vocab_size)      )

		def 			__UpperCAmelCase       (  self      ):
				__a           =			self.prepare_config_and_inputs()

				(
				    (
				    __a
				)      ,     (
				    __a
				)      ,     (
				    __a
				)      ,     (
				    __a
				)      ,     (
				    __a
				)      ,     (
				    __a
				)      ,     (
				    __a
				)      ,     (
				    __a
				)      ,     (
				    __a
				)      ,     
				)           =			config_and_inputs

				__a           =			{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''head_mask''': head_mask}

				return config, inputs_dict





		def 			__UpperCAmelCase       (  self							, _a							, _a							, _a							, _a							, *_a      ):
				__a           =			self.num_labels
				__a           =			CTRLForSequenceClassification(_a      )
				model.to(_a      )
				model.eval()
				__a           =			ids_tensor([self.batch_size]							, self.type_sequence_label_size      )
				__a           =			model(_a							, token_type_ids=_a							, labels=_a      )
				self.parent.assertEqual(result.logits.shape							, (self.batch_size, self.num_labels)      )







@require_torch
class 					__lowerCAmelCase					(   __SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			unittest.TestCase       ):


		'''simple docstring'''



		__UpperCAmelCase		:			str										=				(CTRLModel, CTRLLMHeadModel, CTRLForSequenceClassification) if is_torch_available() else ()
		__UpperCAmelCase		:			Union[str, Any]										=				(CTRLLMHeadModel,) if is_torch_available() else ()
		__UpperCAmelCase		:			Union[str, Any]										=				(
		    {
		        'feature-extraction': CTRLModel,
		        'text-classification': CTRLForSequenceClassification,
		        'text-generation': CTRLLMHeadModel,
		        'zero-shot': CTRLForSequenceClassification,
		    }
		    if is_torch_available()
		    else {}
		)
		__UpperCAmelCase		:			Optional[Any]										=				True
		__UpperCAmelCase		:			List[Any]										=				False
		__UpperCAmelCase		:			str										=				False

		def 			__UpperCAmelCase       (  self							, _a							, _a							, _a							, _a							, _a      ):
				if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
						# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
						# `CTRLConfig` was never used in pipeline tests, either because of a missing checkpoint or because a tiny
						# config could not be created.
						return True

				return False

		def 			__UpperCAmelCase       (  self      ):
				__a           =			CTRLModelTester(self      )
				__a           =			ConfigTester(self							, config_class=_a							, n_embd=37      )

		def 			__UpperCAmelCase       (  self      ):
				super().tearDown()
				# clean-up as much as possible GPU memory occupied by PyTorch
				gc.collect()
				torch.cuda.empty_cache()

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

		def 			__UpperCAmelCase       (  self      ):
				__a           =			self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_ctrl_model(*_a      )

		def 			__UpperCAmelCase       (  self      ):
				__a           =			self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_lm_head_model(*_a      )

		@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''      )
		def 			__UpperCAmelCase       (  self      ):
				pass

		@slow
		def 			__UpperCAmelCase       (  self      ):
				for model_name in CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						__a           =			CTRLModel.from_pretrained(_a      )
						self.assertIsNotNone(_a      )





		@unittest.skip('''The model doesn\'t support left padding'''      )  # and it's not used enough to be worth fixing :)
		def 			__UpperCAmelCase       (  self      ):
				pass







@require_torch
class 					__lowerCAmelCase					(   unittest.TestCase       ):


		'''simple docstring'''



		def 			__UpperCAmelCase       (  self      ):
				super().tearDown()
				# clean-up as much as possible GPU memory occupied by PyTorch
				gc.collect()
				torch.cuda.empty_cache()





		@slow
		def 			__UpperCAmelCase       (  self      ):
				__a           =			CTRLLMHeadModel.from_pretrained('''ctrl'''      )
				model.to(_a      )
				__a           =			torch.tensor(
				    [[11_859, 0, 1_611, 8]]							, dtype=torch.long							, device=_a      )  # Legal the president is
				__a           =			[
				    11_859,
				    0,
				    1_611,
				    8,
				    5,
				    150,
				    26_449,
				    2,
				    19,
				    348,
				    469,
				    3,
				    2_595,
				    48,
				    20_740,
				    246_533,
				    246_533,
				    19,
				    30,
				    5,
				]  # Legal the president is a good guy and I don't want to lose my job. \n \n I have a

				__a           =			model.generate(_a							, do_sample=_a      )
				self.assertListEqual(output_ids[0].tolist()							, _a      )






