





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


_SCREAMING_SNAKE_CASE      	=		logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE      	=		{
    """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_						(					snake_case_					):
      __magic_name__:			Union[str, Any]       =       "marian"
      __magic_name__:			List[Any]       =       ["past_key_values"]
      __magic_name__:			Tuple       =       {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}






      def __init__(      self      :      Optional[Any]		,   _A      :      List[Any]=58101		,   _A      :      List[str]=None		,   _A      :      Tuple=1024		,   _A      :      Any=12		,   _A      :      int=4096		,   _A      :      Optional[Any]=16		,   _A      :      Tuple=12		,   _A      :      int=4096		,   _A      :      Union[str, Any]=16		,   _A      :      Dict=0.0		,   _A      :      List[Any]=0.0		,   _A      :      List[str]=True		,   _A      :      str=True		,   _A      :      List[str]="gelu"		,   _A      :      int=1024		,   _A      :      List[Any]=0.1		,   _A      :      int=0.0		,   _A      :      Union[str, Any]=0.0		,   _A      :      int=0.0_2		,   _A      :      Dict=58100		,   _A      :      Any=False		,   _A      :      Dict=58100		,   _A      :      Tuple=0		,   _A      :      Union[str, Any]=0		,   _A      :      str=True		,   **_A      :      Dict		,   )							->    List[Any]:







       """simple docstring"""


       snake_case_		:			int											=	vocab_size
       snake_case_		:			Dict											=	decoder_vocab_size or vocab_size
       snake_case_		:			int											=	max_position_embeddings
       snake_case_		:			int											=	d_model
       snake_case_		:			Optional[int]											=	encoder_ffn_dim
       snake_case_		:			Optional[Any]											=	encoder_layers
       snake_case_		:			List[str]											=	encoder_attention_heads
       snake_case_		:			int											=	decoder_ffn_dim
       snake_case_		:			Dict											=	decoder_layers
       snake_case_		:			List[Any]											=	decoder_attention_heads
       snake_case_		:			str											=	dropout
       snake_case_		:			Optional[Any]											=	attention_dropout
       snake_case_		:			Tuple											=	activation_dropout
       snake_case_		:			Any											=	activation_function
       snake_case_		:			Optional[int]											=	init_std
       snake_case_		:			str											=	encoder_layerdrop
       snake_case_		:			Optional[int]											=	decoder_layerdrop
       snake_case_		:			int											=	use_cache
       snake_case_		:			List[str]											=	encoder_layers
       snake_case_		:			Union[str, Any]											=	scale_embedding  # scale factor will be sqrt(d_model) if True
       snake_case_		:			Optional[int]											=	share_encoder_decoder_embeddings
       super().__init__(
           pad_token_id=_A		,   eos_token_id=_A		,   is_encoder_decoder=_A		,   decoder_start_token_id=_A		,   forced_eos_token_id=_A		,   **_A		,   )
class SCREAMING_SNAKE_CASE_						(					snake_case_					):






      @property
      # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
      def UpperCAmelCase_				(      self      :      str	)							->    Mapping[str, Mapping[int, str]]:







       """simple docstring"""


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

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

        if self.use_past:
         self.fill_with_past_key_values_(_A		,   direction='inputs'	)
       elif self.task == "causal-lm":
        # TODO: figure this case out.
        snake_case_		:			List[str]											=	OrderedDict(
            [
                ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
            ]	)
        if self.use_past:
         snake_case_		:			int											=	self.num_layers
         for i in range(_A	):
          snake_case_		:			Tuple											=	{0: 'batch', 2: 'past_sequence + sequence'}
          snake_case_		:			Tuple											=	{0: 'batch', 2: 'past_sequence + sequence'}
       else:
        snake_case_		:			Optional[Any]											=	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 UpperCAmelCase_				(      self      :      Union[str, Any]	)							->    Mapping[str, Mapping[int, str]]:







       """simple docstring"""


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






      def UpperCAmelCase_				(      self      :      Tuple		,   _A      :      PreTrainedTokenizer		,   _A      :      int = -1		,   _A      :      int = -1		,   _A      :      bool = False		,   _A      :      Optional[TensorType] = None		,   )							->    Mapping[str, Any]:







       """simple docstring"""


       snake_case_		:			Optional[int]											=	self._generate_dummy_inputs_for_encoder_and_decoder(
           _A		,   _A		,   _A		,   _A		,   _A	)

       # Generate decoder inputs
       snake_case_		:			Optional[Any]											=	seq_length if not self.use_past else 1
       snake_case_		:			Optional[int]											=	self._generate_dummy_inputs_for_encoder_and_decoder(
           _A		,   _A		,   _A		,   _A		,   _A	)
       snake_case_		:			int											=	{F"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
       snake_case_		:			Dict											=	dict(**_A		,   **_A	)

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

        snake_case_		:			Dict											=	torch.cat(
            [common_inputs['decoder_attention_mask'], torch.ones(_A		,   _A	)]		,   dim=1	)

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

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






      def UpperCAmelCase_				(      self      :      Optional[int]		,   _A      :      PreTrainedTokenizer		,   _A      :      int = -1		,   _A      :      int = -1		,   _A      :      bool = False		,   _A      :      Optional[TensorType] = None		,   )							->    Mapping[str, Any]:







       """simple docstring"""


       snake_case_		:			Union[str, Any]											=	self._generate_dummy_inputs_for_encoder_and_decoder(
           _A		,   _A		,   _A		,   _A		,   _A	)

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

        snake_case_		:			str											=	common_inputs['attention_mask'].dtype
        snake_case_		:			int											=	torch.cat(
            [common_inputs['attention_mask'], torch.ones(_A		,   _A		,   dtype=_A	)]		,   dim=1	)
        snake_case_		:			List[Any]											=	[
            (torch.zeros(_A	), torch.zeros(_A	)) for _ in range(_A	)
        ]
       return common_inputs






      def UpperCAmelCase_				(      self      :      str		,   _A      :      PreTrainedTokenizer		,   _A      :      int = -1		,   _A      :      int = -1		,   _A      :      bool = False		,   _A      :      Optional[TensorType] = None		,   )							->    Mapping[str, Any]:







       """simple docstring"""


       snake_case_		:			Optional[int]											=	compute_effective_axis_dimension(
           _A		,   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
       snake_case_		:			Tuple											=	tokenizer.num_special_tokens_to_add(_A	)
       snake_case_		:			List[str]											=	compute_effective_axis_dimension(
           _A		,   fixed_dimension=OnnxConfig.default_fixed_sequence		,   num_token_to_add=_A	)

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






      def UpperCAmelCase_				(      self      :      Optional[int]		,   _A      :      PreTrainedTokenizer		,   _A      :      int = -1		,   _A      :      int = -1		,   _A      :      bool = False		,   _A      :      Optional[TensorType] = None		,   )							->    Mapping[str, Any]:







       """simple docstring"""


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

       else:
        snake_case_		:			int											=	self._generate_dummy_inputs_for_causal_lm(
            _A		,   batch_size=_A		,   seq_length=_A		,   is_pair=_A		,   framework=_A	)

       return common_inputs






      def UpperCAmelCase_				(      self      :      Any		,   _A      :      Tuple		,   _A      :      Tuple		,   _A      :      Union[str, Any]		,   _A      :      List[str]	)							->    Optional[int]:







       """simple docstring"""


       if self.task in ["default", "seq2seq-lm"]:
        snake_case_		:			List[Any]											=	super()._flatten_past_key_values_(_A		,   _A		,   _A		,   _A	)
       else:
        snake_case_		:			Any											=	super(_A		,   self	)._flatten_past_key_values_(
            _A		,   _A		,   _A		,   _A	)






      @property
      def UpperCAmelCase_				(      self      :      Dict	)							->    float:







       """simple docstring"""


       return 1E-4








import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()
class SCREAMING_SNAKE_CASE_						(					unittest.TestCase					):






						@property
						def UpperCAmelCase_				(      self      :      Any	)							->    Optional[Any]:







							"""simple docstring"""


							torch.manual_seed(0	)
							snake_case_		:			List[Any]											=	UNetaDModel(
							    block_out_channels=(32, 64)		,   layers_per_block=2		,   sample_size=32		,   in_channels=3		,   out_channels=3		,   down_block_types=('DownBlock2D', 'AttnDownBlock2D')		,   up_block_types=('AttnUpBlock2D', 'UpBlock2D')		,   )
							return model






						def UpperCAmelCase_				(      self      :      Optional[Any]	)							->    Tuple:







							"""simple docstring"""


							snake_case_		:			List[Any]											=	self.dummy_uncond_unet
							snake_case_		:			Optional[Any]											=	ScoreSdeVeScheduler()

							snake_case_		:			Tuple											=	ScoreSdeVePipeline(unet=_A		,   scheduler=_A	)
							sde_ve.to(_A	)
							sde_ve.set_progress_bar_config(disable=_A	)

							snake_case_		:			Optional[Any]											=	torch.manual_seed(0	)
							snake_case_		:			Tuple											=	sde_ve(num_inference_steps=2		,   output_type='numpy'		,   generator=_A	).images

							snake_case_		:			int											=	torch.manual_seed(0	)
							snake_case_		:			Optional[int]											=	sde_ve(num_inference_steps=2		,   output_type='numpy'		,   generator=_A		,   return_dict=_A	)[
							    0
							]

							snake_case_		:			List[str]											=	image[0, -3:, -3:, -1]
							snake_case_		:			Dict											=	image_from_tuple[0, -3:, -3:, -1]

							assert image.shape == (1, 32, 32, 3)
							snake_case_		:			List[Any]											=	np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]	)

							assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-2
							assert np.abs(image_from_tuple_slice.flatten() - expected_slice	).max() < 1E-2
@slow
@require_torch
class SCREAMING_SNAKE_CASE_						(					unittest.TestCase					):






						def UpperCAmelCase_				(      self      :      Dict	)							->    Dict:







							"""simple docstring"""


							snake_case_		:			Dict											=	'google/ncsnpp-church-256'
							snake_case_		:			List[Any]											=	UNetaDModel.from_pretrained(_A	)

							snake_case_		:			str											=	ScoreSdeVeScheduler.from_pretrained(_A	)

							snake_case_		:			Optional[Any]											=	ScoreSdeVePipeline(unet=_A		,   scheduler=_A	)
							sde_ve.to(_A	)
							sde_ve.set_progress_bar_config(disable=_A	)

							snake_case_		:			Any											=	torch.manual_seed(0	)
							snake_case_		:			Optional[int]											=	sde_ve(num_inference_steps=10		,   output_type='numpy'		,   generator=_A	).images

							snake_case_		:			Union[str, Any]											=	image[0, -3:, -3:, -1]

							assert image.shape == (1, 256, 256, 3)

							snake_case_		:			Dict											=	np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0]	)

							assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-2

