



'''simple docstring'''


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

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


_lowerCamelCase	:       str      	=	logging.get_logger(__name__)

_lowerCamelCase	:       Union[str, Any]      	=	{
    "Helsinki-NLP/opus-mt-en-de": "https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json",
    # See all Marian models at https://huggingface.co/models?filter=marian
}

class  SCREAMING_SNAKE_CASE (  _a		):






    """simple docstring"""





    _SCREAMING_SNAKE_CASE	  =  """marian"""
    _SCREAMING_SNAKE_CASE	  =  ["""past_key_values"""]
    _SCREAMING_SNAKE_CASE	  =  {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}






    def __init__(  self      :					List[str]  ,						UpperCamelCase__      :					Optional[Any]=5_8_1_0_1  ,						UpperCamelCase__      :					Dict=None  ,						UpperCamelCase__      :					Any=1_0_2_4  ,						UpperCamelCase__      :					List[str]=1_2  ,						UpperCamelCase__      :					Dict=4_0_9_6  ,						UpperCamelCase__      :					Tuple=1_6  ,						UpperCamelCase__      :					Any=1_2  ,						UpperCamelCase__      :					List[str]=4_0_9_6  ,						UpperCamelCase__      :					int=1_6  ,						UpperCamelCase__      :					str=0.0  ,						UpperCamelCase__      :					Optional[Any]=0.0  ,						UpperCamelCase__      :					Optional[int]=True  ,						UpperCamelCase__      :					List[Any]=True  ,						UpperCamelCase__      :					Optional[int]="gelu"  ,						UpperCamelCase__      :					List[Any]=1_0_2_4  ,						UpperCamelCase__      :					str=0.1  ,						UpperCamelCase__      :					Dict=0.0  ,						UpperCamelCase__      :					Tuple=0.0  ,						UpperCamelCase__      :					List[str]=0.0_2  ,						UpperCamelCase__      :					List[str]=5_8_1_0_0  ,						UpperCamelCase__      :					Dict=False  ,						UpperCamelCase__      :					Optional[int]=5_8_1_0_0  ,						UpperCamelCase__      :					Union[str, Any]=0  ,						UpperCamelCase__      :					List[str]=0  ,						UpperCamelCase__      :					int=True  ,						**UpperCamelCase__      :					Optional[Any]  ,						):


       """simple docstring"""







       UpperCamelCase					=  vocab_size
       UpperCamelCase					=  decoder_vocab_size or vocab_size
       UpperCamelCase					=  max_position_embeddings
       UpperCamelCase					=  d_model
       UpperCamelCase					=  encoder_ffn_dim
       UpperCamelCase					=  encoder_layers
       UpperCamelCase					=  encoder_attention_heads
       UpperCamelCase					=  decoder_ffn_dim
       UpperCamelCase					=  decoder_layers
       UpperCamelCase					=  decoder_attention_heads
       UpperCamelCase					=  dropout
       UpperCamelCase					=  attention_dropout
       UpperCamelCase					=  activation_dropout
       UpperCamelCase					=  activation_function
       UpperCamelCase					=  init_std
       UpperCamelCase					=  encoder_layerdrop
       UpperCamelCase					=  decoder_layerdrop
       UpperCamelCase					=  use_cache
       UpperCamelCase					=  encoder_layers
       UpperCamelCase					=  scale_embedding  # scale factor will be sqrt(d_model) if True
       UpperCamelCase					=  share_encoder_decoder_embeddings
       super().__init__(
           pad_token_id=UpperCamelCase__  ,						eos_token_id=UpperCamelCase__  ,						is_encoder_decoder=UpperCamelCase__  ,						decoder_start_token_id=UpperCamelCase__  ,						forced_eos_token_id=UpperCamelCase__  ,						**UpperCamelCase__  ,						)

class  SCREAMING_SNAKE_CASE (  _a		):






    """simple docstring"""





    @property
    # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
    def  A       (  self      :					List[str] ):


       """simple docstring"""







       if self.task in ["default", "seq2seq-lm"]:
          UpperCamelCase					=  OrderedDict(
              [
                  ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                  ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
              ] )

          if self.use_past:
             UpperCamelCase					=  {0: 'batch'}
             UpperCamelCase					=  {0: 'batch', 1: 'past_decoder_sequence + sequence'}
          else:
             UpperCamelCase					=  {0: 'batch', 1: 'decoder_sequence'}
             UpperCamelCase					=  {0: 'batch', 1: 'decoder_sequence'}

          if self.use_past:
             self.fill_with_past_key_values_(UpperCamelCase__  ,						direction='inputs' )
       elif self.task == "causal-lm":
          # TODO: figure this case out.
          UpperCamelCase					=  OrderedDict(
              [
                  ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                  ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
              ] )
          if self.use_past:
             UpperCamelCase				,      UpperCamelCase					=  self.num_layers
             for i in range(UpperCamelCase__ ):
                UpperCamelCase					=  {0: 'batch', 2: 'past_sequence + sequence'}
                UpperCamelCase					=  {0: 'batch', 2: 'past_sequence + sequence'}
       else:
          UpperCamelCase					=  OrderedDict(
              [
                  ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                  ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                  ('decoder_input_ids', {0: 'batch', 1: 'decoder_sequence'}),
                  ('decoder_attention_mask', {0: 'batch', 1: 'decoder_sequence'}),
              ] )

       return common_inputs






    @property
    # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
    def  A       (  self      :					Dict ):


       """simple docstring"""







       if self.task in ["default", "seq2seq-lm"]:
          UpperCamelCase					=  super().outputs
       else:
          UpperCamelCase					=  super(UpperCamelCase__  ,						self ).outputs
          if self.use_past:
             UpperCamelCase				,      UpperCamelCase					=  self.num_layers
             for i in range(UpperCamelCase__ ):
                UpperCamelCase					=  {0: 'batch', 2: 'past_sequence + sequence'}
                UpperCamelCase					=  {0: 'batch', 2: 'past_sequence + sequence'}
       return common_outputs






    def  A       (  self      :					List[str]  ,						UpperCamelCase__      :					PreTrainedTokenizer  ,						UpperCamelCase__      :					int = -1  ,						UpperCamelCase__      :					int = -1  ,						UpperCamelCase__      :					bool = False  ,						UpperCamelCase__      :					Optional[TensorType] = None  ,						):


       """simple docstring"""







       UpperCamelCase					=  self._generate_dummy_inputs_for_encoder_and_decoder(
           UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__ )

       # Generate decoder inputs
       UpperCamelCase					=  seq_length if not self.use_past else 1
       UpperCamelCase					=  self._generate_dummy_inputs_for_encoder_and_decoder(
           UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__ )
       UpperCamelCase					=  {f"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
       UpperCamelCase					=  dict(**UpperCamelCase__  ,						**UpperCamelCase__ )

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

          UpperCamelCase					=  torch.cat(
              [common_inputs['decoder_attention_mask'], torch.ones(UpperCamelCase__  ,						UpperCamelCase__ )]  ,						dim=1 )

          UpperCamelCase					=  []
          # If the number of encoder and decoder layers are present in the model configuration, both are considered
          UpperCamelCase				,      UpperCamelCase					=  self.num_layers
          UpperCamelCase					=  min(UpperCamelCase__  ,						UpperCamelCase__ )
          UpperCamelCase					=  max(UpperCamelCase__  ,						UpperCamelCase__ ) - min_num_layers
          UpperCamelCase					=  'encoder' if num_encoder_layers > num_decoder_layers else 'decoder'

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






    def  A       (  self      :					Tuple  ,						UpperCamelCase__      :					PreTrainedTokenizer  ,						UpperCamelCase__      :					int = -1  ,						UpperCamelCase__      :					int = -1  ,						UpperCamelCase__      :					bool = False  ,						UpperCamelCase__      :					Optional[TensorType] = None  ,						):


       """simple docstring"""







       UpperCamelCase					=  self._generate_dummy_inputs_for_encoder_and_decoder(
           UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__ )

       if self.use_past:
          if not is_torch_available():
             raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.' )
          else:
             import torch
          UpperCamelCase				,      UpperCamelCase					=  common_inputs['input_ids'].shape
          # Not using the same length for past_key_values
          UpperCamelCase					=  seqlen + 2
          UpperCamelCase				,      UpperCamelCase					=  self.num_layers
          UpperCamelCase				,      UpperCamelCase					=  self.num_attention_heads
          UpperCamelCase					=  (
              batch,
              num_encoder_attention_heads,
              past_key_values_length,
              self._config.hidden_size // num_encoder_attention_heads,
          )

          UpperCamelCase					=  common_inputs['attention_mask'].dtype
          UpperCamelCase					=  torch.cat(
              [common_inputs['attention_mask'], torch.ones(UpperCamelCase__  ,						UpperCamelCase__  ,						dtype=UpperCamelCase__ )]  ,						dim=1 )
          UpperCamelCase					=  [
              (torch.zeros(UpperCamelCase__ ), torch.zeros(UpperCamelCase__ )) for _ in range(UpperCamelCase__ )
          ]
       return common_inputs






    def  A       (  self      :					int  ,						UpperCamelCase__      :					PreTrainedTokenizer  ,						UpperCamelCase__      :					int = -1  ,						UpperCamelCase__      :					int = -1  ,						UpperCamelCase__      :					bool = False  ,						UpperCamelCase__      :					Optional[TensorType] = None  ,						):


       """simple docstring"""







       UpperCamelCase					=  compute_effective_axis_dimension(
           UpperCamelCase__  ,						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
       UpperCamelCase					=  tokenizer.num_special_tokens_to_add(UpperCamelCase__ )
       UpperCamelCase					=  compute_effective_axis_dimension(
           UpperCamelCase__  ,						fixed_dimension=OnnxConfig.default_fixed_sequence  ,						num_token_to_add=UpperCamelCase__ )

       # Generate dummy inputs according to compute batch and sequence
       UpperCamelCase					=  [' '.join([tokenizer.unk_token] ) * seq_length] * batch_size
       UpperCamelCase					=  dict(tokenizer(UpperCamelCase__  ,						return_tensors=UpperCamelCase__ ) )
       return common_inputs






    def  A       (  self      :					Dict  ,						UpperCamelCase__      :					PreTrainedTokenizer  ,						UpperCamelCase__      :					int = -1  ,						UpperCamelCase__      :					int = -1  ,						UpperCamelCase__      :					bool = False  ,						UpperCamelCase__      :					Optional[TensorType] = None  ,						):


       """simple docstring"""







       if self.task in ["default", "seq2seq-lm"]:
          UpperCamelCase					=  self._generate_dummy_inputs_for_default_and_seqaseq_lm(
              UpperCamelCase__  ,						batch_size=UpperCamelCase__  ,						seq_length=UpperCamelCase__  ,						is_pair=UpperCamelCase__  ,						framework=UpperCamelCase__ )

       else:
          UpperCamelCase					=  self._generate_dummy_inputs_for_causal_lm(
              UpperCamelCase__  ,						batch_size=UpperCamelCase__  ,						seq_length=UpperCamelCase__  ,						is_pair=UpperCamelCase__  ,						framework=UpperCamelCase__ )

       return common_inputs






    def  A       (  self      :					List[str]  ,						UpperCamelCase__      :					Any  ,						UpperCamelCase__      :					List[str]  ,						UpperCamelCase__      :					Tuple  ,						UpperCamelCase__      :					Optional[int] ):


       """simple docstring"""







       if self.task in ["default", "seq2seq-lm"]:
          UpperCamelCase					=  super()._flatten_past_key_values_(UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__ )
       else:
          UpperCamelCase					=  super(UpperCamelCase__  ,						self )._flatten_past_key_values_(
              UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__ )






    @property
    def  A       (  self      :					Dict ):


       """simple docstring"""







       return 1E-4



import os
import re
import shutil
import sys
import tempfile
import unittest

import black


_A   =     os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))

import check_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If DDPMSchedulerOutput is changed in scheduling_ddpm.py, this code needs to be manually updated.
_A   =     "    \"\"\"\n    Output class for the scheduler's step function output.\n\n    Args:\n        prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n            Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the\n            denoising loop.\n        pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n            The predicted denoised sample (x_{0}) based on the model output from the current timestep.\n            `pred_original_sample` can be used to preview progress or for guidance.\n    \"\"\"\n\n    prev_sample: torch.FloatTensor\n    pred_original_sample: Optional[torch.FloatTensor] = None\n"





class 	_lowerCAmelCase					(	unittest.TestCase       ):




				def      __a						(					self     )				->      str:
							lowerCAmelCase_						=	tempfile.mkdtemp()
							os.makedirs(os.path.join(self.diffusers_dir				,				"schedulers/"     )     )
							lowerCAmelCase_						=	self.diffusers_dir
							shutil.copy(
							    os.path.join(_UpperCamelCase				,				"src/diffusers/schedulers/scheduling_ddpm.py"     )				,				os.path.join(self.diffusers_dir				,				"schedulers/scheduling_ddpm.py"     )				,				)




				def      __a						(					self     )				->      Optional[int]:
							lowerCAmelCase_						=	"src/diffusers"
							shutil.rmtree(self.diffusers_dir     )




				def      __a						(					self				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase=None     )				->      Optional[Any]:
							lowerCAmelCase_						=	comment + f"""\nclass {class_name}(nn.Module):\n""" + class_code
							if overwrite_result is not None:
										lowerCAmelCase_						=	comment + f"""\nclass {class_name}(nn.Module):\n""" + overwrite_result
							lowerCAmelCase_						=	black.Mode(target_versions={black.TargetVersion.PYaa}				,				line_length=119     )
							lowerCAmelCase_						=	black.format_str(_UpperCamelCase				,				mode=_UpperCamelCase     )
							lowerCAmelCase_						=	os.path.join(self.diffusers_dir				,				"new_code.py"     )
							with open(_UpperCamelCase				,				"w"				,				newline="\n"     ) as f:
										f.write(_UpperCamelCase     )
							if overwrite_result is None:
										self.assertTrue(len(check_copies.is_copy_consistent(_UpperCamelCase     )     ) == 0     )
							else:
										check_copies.is_copy_consistent(f.name				,				overwrite=_UpperCamelCase     )
										with open(_UpperCamelCase				,				"r"     ) as f:
													self.assertTrue(f.read()				,				_UpperCamelCase     )




				def      __a						(					self     )				->      Optional[Any]:
							lowerCAmelCase_						=	check_copies.find_code_in_diffusers("schedulers.scheduling_ddpm.DDPMSchedulerOutput"     )
							self.assertEqual(_UpperCamelCase				,				_UpperCamelCase     )




				def      __a						(					self     )				->      Tuple:
							# Base copy consistency
							self.check_copy_consistency(
							    "# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput"				,				"DDPMSchedulerOutput"				,				REFERENCE_CODE + "\n"				,				)

							# With no empty line at the end
							self.check_copy_consistency(
							    "# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput"				,				"DDPMSchedulerOutput"				,				_UpperCamelCase				,				)

							# Copy consistency with rename
							self.check_copy_consistency(
							    "# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test"				,				"TestSchedulerOutput"				,				re.sub("DDPM"				,				"Test"				,				_UpperCamelCase     )				,				)

							# Copy consistency with a really long name
							lowerCAmelCase_						=	"TestClassWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason"
							self.check_copy_consistency(
							    f"""# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->{long_class_name}"""				,				f"""{long_class_name}SchedulerOutput"""				,				re.sub("Bert"				,				_UpperCamelCase				,				_UpperCamelCase     )				,				)

							# Copy consistency with overwrite
							self.check_copy_consistency(
							    "# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test"				,				"TestSchedulerOutput"				,				_UpperCamelCase				,				overwrite_result=re.sub("DDPM"				,				"Test"				,				_UpperCamelCase     )				,				)



