





"""simple docstring"""





import os
import tempfile
import unittest

import numpy as np
import torch

from diffusers.models import ModelMixin, UNetaDConditionModel
from diffusers.models.attention_processor import AttnProcessor, LoRAAttnProcessor
from diffusers.utils import (
    floats_tensor,
    logging,
    skip_mps,
    torch_device,
)
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()

_lowerCamelCase       =	logging.get_logger(__name__)




def 		lowerCAmelCase_	(  lowercase_   :							List[Any] ,      lowercase_   :							bool = True		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE :			str												=       {}
 for name in model.attn_processors.keys():
  __SCREAMING_SNAKE_CASE :			int												=       name.endswith('''attn2.processor'''		) and not (
      name.startswith('''transformer_in'''		) or '''temp_attentions''' in name.split('''.'''		)
  )
  __SCREAMING_SNAKE_CASE :			int												=       model.config.cross_attention_dim if has_cross_attention else None
  if name.startswith('''mid_block'''		):
   __SCREAMING_SNAKE_CASE :			Any												=       model.config.block_out_channels[-1]
  elif name.startswith('''up_blocks'''		):
   __SCREAMING_SNAKE_CASE :			List[str]												=       int(name[len('''up_blocks.'''		)]		)
   __SCREAMING_SNAKE_CASE :			Dict												=       list(reversed(model.config.block_out_channels		)		)[block_id]
  elif name.startswith('''down_blocks'''		):
   __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       int(name[len('''down_blocks.'''		)]		)
   __SCREAMING_SNAKE_CASE :			Optional[Any]												=       model.config.block_out_channels[block_id]
  elif name.startswith('''transformer_in'''		):
   # Note that the `8 * ...` comes from: https://github.com/huggingface/diffusers/blob/7139f0e874f10b2463caa8cbd585762a309d12d6/src/diffusers/models/unet_3d_condition.py#L148
   __SCREAMING_SNAKE_CASE :			Optional[Any]												=       8 * model.config.attention_head_dim

  __SCREAMING_SNAKE_CASE :			str												=       LoRAAttnProcessor(hidden_size=lowercase_ ,      cross_attention_dim=lowercase_		)
  __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       lora_attn_procs[name].to(model.device		)

  if mock_weights:
   # add 1 to weights to mock trained weights
   with torch.no_grad():
    lora_attn_procs[name].to_q_lora.up.weight += 1
    lora_attn_procs[name].to_k_lora.up.weight += 1
    lora_attn_procs[name].to_v_lora.up.weight += 1
    lora_attn_procs[name].to_out_lora.up.weight += 1

 return lora_attn_procs






@skip_mps
class 			snake_case		(   __UpperCAmelCase ,     __UpperCAmelCase ,     unittest.TestCase   ):
      lowerCamelCase__						= UNetaDConditionModel
      lowerCamelCase__						= '''sample'''

      @property
      def 						SCREAMING_SNAKE_CASE_			(			self   :Union[str, Any]						):
       __SCREAMING_SNAKE_CASE :			Optional[Any]												=       4
       __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       4
       __SCREAMING_SNAKE_CASE :			Dict												=       4
       __SCREAMING_SNAKE_CASE :			Optional[int]												=       (3_2, 3_2)

       __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       floats_tensor((batch_size, num_channels, num_frames) + sizes						).to(_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE :			Optional[int]												=       torch.tensor([1_0]						).to(_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE :			str												=       floats_tensor((batch_size, 4, 3_2)						).to(_lowerCamelCase						)

       return {"sample": noise, "timestep": time_step, "encoder_hidden_states": encoder_hidden_states}

      @property
      def 						SCREAMING_SNAKE_CASE_			(			self   :Any						):
       return (4, 4, 3_2, 3_2)

      @property
      def 						SCREAMING_SNAKE_CASE_			(			self   :Tuple						):
       return (4, 4, 3_2, 3_2)

      def 						SCREAMING_SNAKE_CASE_			(			self   :Optional[Any]						):
       __SCREAMING_SNAKE_CASE :			int												=       {
           '''block_out_channels''': (3_2, 6_4),
           '''down_block_types''': (
               '''CrossAttnDownBlock3D''',
               '''DownBlock3D''',
           ),
           '''up_block_types''': ('''UpBlock3D''', '''CrossAttnUpBlock3D'''),
           '''cross_attention_dim''': 3_2,
           '''attention_head_dim''': 8,
           '''out_channels''': 4,
           '''in_channels''': 4,
           '''layers_per_block''': 1,
           '''sample_size''': 3_2,
       }
       __SCREAMING_SNAKE_CASE :			List[Any]												=       self.dummy_input
       return init_dict, inputs_dict

      @unittest.skipIf(
          torch_device != '''cuda''' or not is_xformers_available()						,							reason='''XFormers attention is only available with CUDA and `xformers` installed'''						,							)
      def 						SCREAMING_SNAKE_CASE_			(			self   :str						):
       __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE :			Dict												=       self.prepare_init_args_and_inputs_for_common()
       __SCREAMING_SNAKE_CASE :			Dict												=       self.model_class(**_lowerCamelCase						)

       model.enable_xformers_memory_efficient_attention()

       assert (
           model.mid_block.attentions[0].transformer_blocks[0].attna.processor.__class__.__name__
           == "XFormersAttnProcessor"
       ), "xformers is not enabled"

      def 						SCREAMING_SNAKE_CASE_			(			self   :List[Any]						):
       __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE :			Dict												=       self.prepare_init_args_and_inputs_for_common()

       __SCREAMING_SNAKE_CASE :			Dict												=       3_2

       __SCREAMING_SNAKE_CASE :			Tuple												=       self.model_class(**_lowerCamelCase						)
       model.to(_lowerCamelCase						)
       model.eval()

       with torch.no_grad():
        __SCREAMING_SNAKE_CASE :			str												=       model(**_lowerCamelCase						)

        if isinstance(_lowerCamelCase						,							_lowerCamelCase						):
         __SCREAMING_SNAKE_CASE :			Optional[Any]												=       output.sample

       self.assertIsNotNone(_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE :			Dict												=       inputs_dict['''sample'''].shape
       self.assertEqual(output.shape						,							_lowerCamelCase						,							'''Input and output shapes do not match'''						)

      def 						SCREAMING_SNAKE_CASE_			(			self   :List[str]						):
       __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE :			Tuple												=       self.prepare_init_args_and_inputs_for_common()
       __SCREAMING_SNAKE_CASE :			Tuple												=       self.model_class(**_lowerCamelCase						)
       model.to(_lowerCamelCase						)
       model.eval()

       with torch.no_grad():
        # Warmup pass when using mps (see #372)
        if torch_device == "mps" and isinstance(_lowerCamelCase						,							_lowerCamelCase						):
         model(**self.dummy_input						)

        __SCREAMING_SNAKE_CASE :			Optional[int]												=       model(**_lowerCamelCase						)
        if isinstance(_lowerCamelCase						,							_lowerCamelCase						):
         __SCREAMING_SNAKE_CASE :			Any												=       first.sample

        __SCREAMING_SNAKE_CASE :			Any												=       model(**_lowerCamelCase						)
        if isinstance(_lowerCamelCase						,							_lowerCamelCase						):
         __SCREAMING_SNAKE_CASE :			int												=       second.sample

       __SCREAMING_SNAKE_CASE :			Optional[Any]												=       first.cpu().numpy()
       __SCREAMING_SNAKE_CASE :			Optional[int]												=       second.cpu().numpy()
       __SCREAMING_SNAKE_CASE :			Optional[int]												=       out_a[~np.isnan(_lowerCamelCase						)]
       __SCREAMING_SNAKE_CASE :			int												=       out_a[~np.isnan(_lowerCamelCase						)]
       __SCREAMING_SNAKE_CASE :			int												=       np.amax(np.abs(out_a - out_a						)						)
       self.assertLessEqual(_lowerCamelCase						,							1e-5						)

      def 						SCREAMING_SNAKE_CASE_			(			self   :Optional[Any]						):
       __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE :			List[Any]												=       self.prepare_init_args_and_inputs_for_common()

       __SCREAMING_SNAKE_CASE :			Tuple												=       8

       __SCREAMING_SNAKE_CASE :			Tuple												=       self.model_class(**_lowerCamelCase						)
       model.to(_lowerCamelCase						)
       model.eval()

       model.set_attention_slice('''auto'''						)
       with torch.no_grad():
        __SCREAMING_SNAKE_CASE :			str												=       model(**_lowerCamelCase						)
       assert output is not None

       model.set_attention_slice('''max'''						)
       with torch.no_grad():
        __SCREAMING_SNAKE_CASE :			List[str]												=       model(**_lowerCamelCase						)
       assert output is not None

       model.set_attention_slice(2						)
       with torch.no_grad():
        __SCREAMING_SNAKE_CASE :			List[Any]												=       model(**_lowerCamelCase						)
       assert output is not None

      def 						SCREAMING_SNAKE_CASE_			(			self   :Optional[int]						):
       __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE :			Optional[Any]												=       self.prepare_init_args_and_inputs_for_common()

       __SCREAMING_SNAKE_CASE :			List[str]												=       8

       __SCREAMING_SNAKE_CASE :			List[str]												=       self.model_class(**_lowerCamelCase						)
       model.to(_lowerCamelCase						)

       with torch.no_grad():
        __SCREAMING_SNAKE_CASE :			List[str]												=       model(**_lowerCamelCase						).sample

       __SCREAMING_SNAKE_CASE :			str												=       create_lora_layers(_lowerCamelCase						)

       # make sure we can set a list of attention processors
       model.set_attn_processor(_lowerCamelCase						)
       model.to(_lowerCamelCase						)

       # test that attn processors can be set to itself
       model.set_attn_processor(model.attn_processors						)

       with torch.no_grad():
        __SCREAMING_SNAKE_CASE :			Tuple												=       model(**_lowerCamelCase						,							cross_attention_kwargs={'''scale''': 0.0}						).sample
        __SCREAMING_SNAKE_CASE :			Tuple												=       model(**_lowerCamelCase						,							cross_attention_kwargs={'''scale''': 0.5}						).sample
        __SCREAMING_SNAKE_CASE :			Optional[Any]												=       model(**_lowerCamelCase						,							cross_attention_kwargs={'''scale''': 0.5}						).sample

       assert (samplea - samplea).abs().max() < 3e-3
       assert (samplea - samplea).abs().max() < 3e-3

       # sample 2 and sample 3 should be different
       assert (samplea - samplea).abs().max() > 3e-3

      def 						SCREAMING_SNAKE_CASE_			(			self   :List[Any]						):
       __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       self.prepare_init_args_and_inputs_for_common()

       __SCREAMING_SNAKE_CASE :			int												=       8

       torch.manual_seed(0						)
       __SCREAMING_SNAKE_CASE :			List[str]												=       self.model_class(**_lowerCamelCase						)
       model.to(_lowerCamelCase						)

       with torch.no_grad():
        __SCREAMING_SNAKE_CASE :			Tuple												=       model(**_lowerCamelCase						).sample

       __SCREAMING_SNAKE_CASE :			int												=       create_lora_layers(_lowerCamelCase						)
       model.set_attn_processor(_lowerCamelCase						)

       with torch.no_grad():
        __SCREAMING_SNAKE_CASE :			Optional[Any]												=       model(**_lowerCamelCase						,							cross_attention_kwargs={'''scale''': 0.5}						).sample

       with tempfile.TemporaryDirectory() as tmpdirname:
        model.save_attn_procs(_lowerCamelCase						)
        self.assertTrue(os.path.isfile(os.path.join(_lowerCamelCase						,							'''pytorch_lora_weights.bin'''						)						)						)
        torch.manual_seed(0						)
        __SCREAMING_SNAKE_CASE :			List[str]												=       self.model_class(**_lowerCamelCase						)
        new_model.to(_lowerCamelCase						)
        new_model.load_attn_procs(_lowerCamelCase						)

       with torch.no_grad():
        __SCREAMING_SNAKE_CASE :			Dict												=       new_model(**_lowerCamelCase						,							cross_attention_kwargs={'''scale''': 0.5}						).sample

       assert (sample - new_sample).abs().max() < 1e-3

       # LoRA and no LoRA should NOT be the same
       assert (sample - old_sample).abs().max() > 1e-4

      def 						SCREAMING_SNAKE_CASE_			(			self   :int						):
       __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE :			List[str]												=       self.prepare_init_args_and_inputs_for_common()

       __SCREAMING_SNAKE_CASE :			List[Any]												=       8

       torch.manual_seed(0						)
       __SCREAMING_SNAKE_CASE :			Optional[Any]												=       self.model_class(**_lowerCamelCase						)
       model.to(_lowerCamelCase						)

       with torch.no_grad():
        __SCREAMING_SNAKE_CASE :			str												=       model(**_lowerCamelCase						).sample

       __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       create_lora_layers(_lowerCamelCase						)
       model.set_attn_processor(_lowerCamelCase						)

       with torch.no_grad():
        __SCREAMING_SNAKE_CASE :			List[str]												=       model(**_lowerCamelCase						,							cross_attention_kwargs={'''scale''': 0.5}						).sample

       with tempfile.TemporaryDirectory() as tmpdirname:
        model.save_attn_procs(_lowerCamelCase						,							safe_serialization=_lowerCamelCase						)
        self.assertTrue(os.path.isfile(os.path.join(_lowerCamelCase						,							'''pytorch_lora_weights.safetensors'''						)						)						)
        torch.manual_seed(0						)
        __SCREAMING_SNAKE_CASE :			Dict												=       self.model_class(**_lowerCamelCase						)
        new_model.to(_lowerCamelCase						)
        new_model.load_attn_procs(_lowerCamelCase						)

       with torch.no_grad():
        __SCREAMING_SNAKE_CASE :			Dict												=       new_model(**_lowerCamelCase						,							cross_attention_kwargs={'''scale''': 0.5}						).sample

       assert (sample - new_sample).abs().max() < 3e-3

       # LoRA and no LoRA should NOT be the same
       assert (sample - old_sample).abs().max() > 1e-4

      def 						SCREAMING_SNAKE_CASE_			(			self   :str						):
       # enable deterministic behavior for gradient checkpointing
       __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE :			Any												=       self.prepare_init_args_and_inputs_for_common()

       __SCREAMING_SNAKE_CASE :			Any												=       8

       torch.manual_seed(0						)
       __SCREAMING_SNAKE_CASE :			Dict												=       self.model_class(**_lowerCamelCase						)
       model.to(_lowerCamelCase						)

       __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       create_lora_layers(_lowerCamelCase						,							mock_weights=_lowerCamelCase						)
       model.set_attn_processor(_lowerCamelCase						)
       # Saving as torch, properly reloads with directly filename
       with tempfile.TemporaryDirectory() as tmpdirname:
        model.save_attn_procs(_lowerCamelCase						)
        self.assertTrue(os.path.isfile(os.path.join(_lowerCamelCase						,							'''pytorch_lora_weights.bin'''						)						)						)
        torch.manual_seed(0						)
        __SCREAMING_SNAKE_CASE :			List[Any]												=       self.model_class(**_lowerCamelCase						)
        new_model.to(_lowerCamelCase						)
        new_model.load_attn_procs(_lowerCamelCase						,							weight_name='''pytorch_lora_weights.bin'''						)

      def 						SCREAMING_SNAKE_CASE_			(			self   :Optional[Any]						):
       __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE :			Tuple												=       self.prepare_init_args_and_inputs_for_common()

       __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       8

       torch.manual_seed(0						)
       __SCREAMING_SNAKE_CASE :			Optional[Any]												=       self.model_class(**_lowerCamelCase						)
       model.to(_lowerCamelCase						)

       __SCREAMING_SNAKE_CASE :			str												=       create_lora_layers(_lowerCamelCase						,							mock_weights=_lowerCamelCase						)
       model.set_attn_processor(_lowerCamelCase						)
       # Saving as torch, properly reloads with directly filename
       with tempfile.TemporaryDirectory() as tmpdirname:
        model.save_attn_procs(_lowerCamelCase						)
        self.assertTrue(os.path.isfile(os.path.join(_lowerCamelCase						,							'''pytorch_lora_weights.bin'''						)						)						)
        torch.manual_seed(0						)
        __SCREAMING_SNAKE_CASE :			Optional[int]												=       self.model_class(**_lowerCamelCase						)
        new_model.to(_lowerCamelCase						)
        with self.assertRaises(_lowerCamelCase						) as e:
         new_model.load_attn_procs(_lowerCamelCase						,							use_safetensors=_lowerCamelCase						)
        self.assertIn('''Error no file named pytorch_lora_weights.safetensors'''						,							str(e.exception						)						)

      def 						SCREAMING_SNAKE_CASE_			(			self   :Any						):
       __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE :			Any												=       self.prepare_init_args_and_inputs_for_common()

       __SCREAMING_SNAKE_CASE :			Any												=       8

       torch.manual_seed(0						)
       __SCREAMING_SNAKE_CASE :			Tuple												=       self.model_class(**_lowerCamelCase						)
       model.to(_lowerCamelCase						)

       with torch.no_grad():
        __SCREAMING_SNAKE_CASE :			str												=       model(**_lowerCamelCase						).sample

       __SCREAMING_SNAKE_CASE :			int												=       create_lora_layers(_lowerCamelCase						)
       model.set_attn_processor(_lowerCamelCase						)

       with torch.no_grad():
        __SCREAMING_SNAKE_CASE :			Tuple												=       model(**_lowerCamelCase						,							cross_attention_kwargs={'''scale''': 0.0}						).sample

       model.set_attn_processor(AttnProcessor()						)

       with torch.no_grad():
        __SCREAMING_SNAKE_CASE :			List[Any]												=       model(**_lowerCamelCase						).sample

       assert (sample - new_sample).abs().max() < 1e-4
       assert (sample - old_sample).abs().max() < 3e-3

      @unittest.skipIf(
          torch_device != '''cuda''' or not is_xformers_available()						,							reason='''XFormers attention is only available with CUDA and `xformers` installed'''						,							)
      def 						SCREAMING_SNAKE_CASE_			(			self   :str						):
       # enable deterministic behavior for gradient checkpointing
       __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE :			Tuple												=       self.prepare_init_args_and_inputs_for_common()

       __SCREAMING_SNAKE_CASE :			Optional[int]												=       4

       torch.manual_seed(0						)
       __SCREAMING_SNAKE_CASE :			Dict												=       self.model_class(**_lowerCamelCase						)
       model.to(_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE :			List[Any]												=       create_lora_layers(_lowerCamelCase						)
       model.set_attn_processor(_lowerCamelCase						)

       # default
       with torch.no_grad():
        __SCREAMING_SNAKE_CASE :			Dict												=       model(**_lowerCamelCase						).sample

        model.enable_xformers_memory_efficient_attention()
        __SCREAMING_SNAKE_CASE :			Optional[Any]												=       model(**_lowerCamelCase						).sample

        model.disable_xformers_memory_efficient_attention()
        __SCREAMING_SNAKE_CASE :			str												=       model(**_lowerCamelCase						).sample

       assert (sample - on_sample).abs().max() < 1e-4
       assert (sample - off_sample).abs().max() < 1e-4



      def 						SCREAMING_SNAKE_CASE_			(			self   :List[str]						):
       __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE :			Optional[int]												=       self.prepare_init_args_and_inputs_for_common()
       __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       3_2

       __SCREAMING_SNAKE_CASE :			Tuple												=       self.model_class(**_lowerCamelCase						)
       model.to(_lowerCamelCase						)
       model.eval()

       with torch.no_grad():
        __SCREAMING_SNAKE_CASE :			List[Any]												=       model(**_lowerCamelCase						)[0]

       model.enable_forward_chunking()
       with torch.no_grad():
        __SCREAMING_SNAKE_CASE :			Any												=       model(**_lowerCamelCase						)[0]

       self.assertEqual(output.shape						,							output_a.shape						,							'''Shape doesn\'t match'''						)
       assert np.abs(output.cpu() - output_a.cpu()						).max() < 1e-2


# (todo: sayakpaul) implement SLOW tests.








"""simple docstring"""





from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


_lowerCamelCase       =	logging.get_logger(__name__)

_lowerCamelCase       =	{
    '''facebook/m2m100_418M''': '''https://huggingface.co/facebook/m2m100_418M/resolve/main/config.json''',
    # See all M2M100 models at https://huggingface.co/models?filter=m2m_100
}





class 			snake_case		(   __UpperCAmelCase   ):
      lowerCamelCase__						= '''m2m_100'''
      lowerCamelCase__						= ['''past_key_values''']
      lowerCamelCase__						= {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}

      def __init__(			self   :int						,							_lowerCamelCase   :Union[str, Any]=1_2_8_1_1_2						,							_lowerCamelCase   :str=1_0_2_4						,							_lowerCamelCase   :Any=1_2						,							_lowerCamelCase   :Dict=4_0_9_6						,							_lowerCamelCase   :List[str]=1_6						,							_lowerCamelCase   :Union[str, Any]=1_2						,							_lowerCamelCase   :Any=4_0_9_6						,							_lowerCamelCase   :str=1_6						,							_lowerCamelCase   :Union[str, Any]=0.0_5						,							_lowerCamelCase   :Any=0.0_5						,							_lowerCamelCase   :int=True						,							_lowerCamelCase   :Dict=True						,							_lowerCamelCase   :List[str]="relu"						,							_lowerCamelCase   :List[Any]=1_0_2_4						,							_lowerCamelCase   :Dict=0.1						,							_lowerCamelCase   :List[Any]=0.1						,							_lowerCamelCase   :Any=0.0						,							_lowerCamelCase   :Tuple=0.0_2						,							_lowerCamelCase   :Dict=2						,							_lowerCamelCase   :List[str]=True						,							_lowerCamelCase   :Any=1						,							_lowerCamelCase   :Dict=0						,							_lowerCamelCase   :List[Any]=2						,							**_lowerCamelCase   :Optional[Any]						,							):
       __SCREAMING_SNAKE_CASE :			Any												=       vocab_size
       __SCREAMING_SNAKE_CASE :			Any												=       max_position_embeddings
       __SCREAMING_SNAKE_CASE :			int												=       d_model
       __SCREAMING_SNAKE_CASE :			List[Any]												=       encoder_ffn_dim
       __SCREAMING_SNAKE_CASE :			Dict												=       encoder_layers
       __SCREAMING_SNAKE_CASE :			int												=       encoder_attention_heads
       __SCREAMING_SNAKE_CASE :			Dict												=       decoder_ffn_dim
       __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       decoder_layers
       __SCREAMING_SNAKE_CASE :			Optional[Any]												=       decoder_attention_heads
       __SCREAMING_SNAKE_CASE :			Optional[Any]												=       dropout
       __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       attention_dropout
       __SCREAMING_SNAKE_CASE :			List[str]												=       activation_dropout
       __SCREAMING_SNAKE_CASE :			List[Any]												=       activation_function
       __SCREAMING_SNAKE_CASE :			Any												=       init_std
       __SCREAMING_SNAKE_CASE :			List[Any]												=       encoder_layerdrop
       __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       decoder_layerdrop
       __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       use_cache
       __SCREAMING_SNAKE_CASE :			List[str]												=       encoder_layers
       __SCREAMING_SNAKE_CASE :			str												=       scale_embedding  # scale factor will be sqrt(d_model) if True

       super().__init__(
           pad_token_id=_lowerCamelCase						,							bos_token_id=_lowerCamelCase						,							eos_token_id=_lowerCamelCase						,							is_encoder_decoder=_lowerCamelCase						,							decoder_start_token_id=_lowerCamelCase						,							**_lowerCamelCase						,							)







class 			snake_case		(   __UpperCAmelCase   ):

      @property
      def 						SCREAMING_SNAKE_CASE_			(			self   :int						):
       __SCREAMING_SNAKE_CASE :			str												=       OrderedDict(
           [
               ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
               ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
           ]						)

       if self.use_past:
        __SCREAMING_SNAKE_CASE :			Dict												=       {0: '''batch'''}
        __SCREAMING_SNAKE_CASE :			Any												=       {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
       else:
        __SCREAMING_SNAKE_CASE :			int												=       {0: '''batch''', 1: '''decoder_sequence'''}
        __SCREAMING_SNAKE_CASE :			List[Any]												=       {0: '''batch''', 1: '''decoder_sequence'''}

       if self.use_past:
        self.fill_with_past_key_values_(_lowerCamelCase						,							direction='''inputs'''						)
       return common_inputs

      def 						SCREAMING_SNAKE_CASE_			(			self   :Tuple						,							_lowerCamelCase   :PreTrainedTokenizer						,							_lowerCamelCase   :int = -1						,							_lowerCamelCase   :int = -1						,							_lowerCamelCase   :bool = False						,							_lowerCamelCase   :Optional[TensorType] = None						,							):
       # Copied from OnnxConfig.generate_dummy_inputs
       # Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
       # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
       __SCREAMING_SNAKE_CASE :			List[Any]												=       compute_effective_axis_dimension(
           _lowerCamelCase						,							fixed_dimension=OnnxConfig.default_fixed_batch						,							num_token_to_add=0						)

       # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
       __SCREAMING_SNAKE_CASE :			List[str]												=       tokenizer.num_special_tokens_to_add(_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE :			int												=       compute_effective_axis_dimension(
           _lowerCamelCase						,							fixed_dimension=OnnxConfig.default_fixed_sequence						,							num_token_to_add=_lowerCamelCase						)

       # Generate dummy inputs according to compute batch and sequence
       __SCREAMING_SNAKE_CASE :			int												=       [''' '''.join([tokenizer.unk_token]						) * seq_length] * batch_size
       __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       dict(tokenizer(_lowerCamelCase						,							return_tensors=_lowerCamelCase						)						)
       return common_inputs

      def 						SCREAMING_SNAKE_CASE_			(			self   :str						,							_lowerCamelCase   :PreTrainedTokenizer						,							_lowerCamelCase   :int = -1						,							_lowerCamelCase   :int = -1						,							_lowerCamelCase   :bool = False						,							_lowerCamelCase   :Optional[TensorType] = None						,							):
       __SCREAMING_SNAKE_CASE :			Dict												=       self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           _lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						)

       # Generate decoder inputs
       __SCREAMING_SNAKE_CASE :			Dict												=       seq_length if not self.use_past else 1
       __SCREAMING_SNAKE_CASE :			Any												=       self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           _lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE :			Optional[int]												=       {f'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
       __SCREAMING_SNAKE_CASE :			Optional[Any]												=       dict(**_lowerCamelCase						,							**_lowerCamelCase						)

       if self.use_past:
        if not is_torch_available():
         raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''						)
        else:
         import torch
        __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE :			int												=       common_inputs['''input_ids'''].shape
        __SCREAMING_SNAKE_CASE :			Tuple												=       common_inputs['''decoder_input_ids'''].shape[1]
        __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE :			Optional[Any]												=       self.num_attention_heads
        __SCREAMING_SNAKE_CASE :			List[str]												=       (
            batch,
            num_encoder_attention_heads,
            encoder_seq_length,
            self._config.hidden_size // num_encoder_attention_heads,
        )
        __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       decoder_seq_length + 3
        __SCREAMING_SNAKE_CASE :			List[str]												=       (
            batch,
            num_decoder_attention_heads,
            decoder_past_length,
            self._config.hidden_size // num_decoder_attention_heads,
        )

        __SCREAMING_SNAKE_CASE :			Any												=       torch.cat(
            [common_inputs['''decoder_attention_mask'''], torch.ones(_lowerCamelCase						,							_lowerCamelCase						)]						,							dim=1						)

        __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       []
        # If the number of encoder and decoder layers are present in the model configuration, both are considered
        __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       self.num_layers
        __SCREAMING_SNAKE_CASE :			Any												=       min(_lowerCamelCase						,							_lowerCamelCase						)
        __SCREAMING_SNAKE_CASE :			Tuple												=       max(_lowerCamelCase						,							_lowerCamelCase						) - min_num_layers
        __SCREAMING_SNAKE_CASE :			Optional[int]												=       '''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''

        for _ in range(_lowerCamelCase						):
         common_inputs["past_key_values"].append(
             (
                 torch.zeros(_lowerCamelCase						),
                 torch.zeros(_lowerCamelCase						),
                 torch.zeros(_lowerCamelCase						),
                 torch.zeros(_lowerCamelCase						),
             )						)
        # TODO: test this.
        __SCREAMING_SNAKE_CASE :			Any												=       encoder_shape if remaining_side_name == '''encoder''' else decoder_shape
        for _ in range(_lowerCamelCase						,							_lowerCamelCase						):
         common_inputs["past_key_values"].append((torch.zeros(_lowerCamelCase						), torch.zeros(_lowerCamelCase						))						)
       return common_inputs



      lowerCamelCase__						= _generate_dummy_inputs_for_default_and_seqaseq_lm

