




'''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


UpperCAmelCase_ 		=      logging.get_logger(__name__)

UpperCAmelCase_ 		=      {
    """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    __lowercase						(  __magic_name__ ):
   _a							=      "marian"
   _a							=      ["past_key_values"]
   _a							=      {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}







   def __init__(	self      ,							UpperCamelCase=5_8101      ,							UpperCamelCase=None      ,							UpperCamelCase=1024      ,							UpperCamelCase=12      ,							UpperCamelCase=4096      ,							UpperCamelCase=16      ,							UpperCamelCase=12      ,							UpperCamelCase=4096      ,							UpperCamelCase=16      ,							UpperCamelCase=0.0      ,							UpperCamelCase=0.0      ,							UpperCamelCase=True      ,							UpperCamelCase=True      ,							UpperCamelCase="gelu"      ,							UpperCamelCase=1024      ,							UpperCamelCase=0.1      ,							UpperCamelCase=0.0      ,							UpperCamelCase=0.0      ,							UpperCamelCase=0.02      ,							UpperCamelCase=5_8100      ,							UpperCamelCase=False      ,							UpperCamelCase=5_8100      ,							UpperCamelCase=0      ,							UpperCamelCase=0      ,							UpperCamelCase=True      ,							**UpperCamelCase      ,							)				->    Optional[int]:
     __a			=					vocab_size
     __a			=					decoder_vocab_size or vocab_size
     __a			=					max_position_embeddings
     __a			=					d_model
     __a			=					encoder_ffn_dim
     __a			=					encoder_layers
     __a			=					encoder_attention_heads
     __a			=					decoder_ffn_dim
     __a			=					decoder_layers
     __a			=					decoder_attention_heads
     __a			=					dropout
     __a			=					attention_dropout
     __a			=					activation_dropout
     __a			=					activation_function
     __a			=					init_std
     __a			=					encoder_layerdrop
     __a			=					decoder_layerdrop
     __a			=					use_cache
     __a			=					encoder_layers
     __a			=					scale_embedding  # scale factor will be sqrt(d_model) if True
     __a			=					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    __lowercase						(  __magic_name__ ):







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




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

       if self.use_past:
         __a			=					{0: 'batch'}
         __a			=					{0: 'batch', 1: 'past_decoder_sequence + sequence'}
       else:
         __a			=					{0: 'batch', 1: 'decoder_sequence'}
         __a			=					{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.
       __a			=					OrderedDict(
           [
               ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
               ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
           ]      )
       if self.use_past:
         __a	,       __a			=					self.num_layers
         for i in range(UpperCamelCase      ):
           __a			=					{0: 'batch', 2: 'past_sequence + sequence'}
           __a			=					{0: 'batch', 2: 'past_sequence + sequence'}
     else:
       __a			=					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      )				->    Mapping[str, Mapping[int, str]]:




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







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase = -1      ,							UpperCamelCase = -1      ,							UpperCamelCase = False      ,							UpperCamelCase = None      ,							)				->    Mapping[str, Any]:
     __a			=					self._generate_dummy_inputs_for_encoder_and_decoder(
         UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      )

     # Generate decoder inputs
     __a			=					seq_length if not self.use_past else 1
     __a			=					self._generate_dummy_inputs_for_encoder_and_decoder(
         UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      )
     __a			=					{f"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
     __a			=					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
       __a	,       __a			=					common_inputs['input_ids'].shape
       __a			=					common_inputs['decoder_input_ids'].shape[1]
       __a	,       __a			=					self.num_attention_heads
       __a			=					(
           batch,
           num_encoder_attention_heads,
           encoder_seq_length,
           self._config.hidden_size // num_encoder_attention_heads,
       )
       __a			=					decoder_seq_length + 3
       __a			=					(
           batch,
           num_decoder_attention_heads,
           decoder_past_length,
           self._config.hidden_size // num_decoder_attention_heads,
       )

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

       __a			=					[]
       # If the number of encoder and decoder layers are present in the model configuration, both are considered
       __a	,       __a			=					self.num_layers
       __a			=					min(UpperCamelCase      ,							UpperCamelCase      )
       __a			=					max(UpperCamelCase      ,							UpperCamelCase      ) - min_num_layers
       __a			=					'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.
       __a			=					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 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase = -1      ,							UpperCamelCase = -1      ,							UpperCamelCase = False      ,							UpperCamelCase = None      ,							)				->    Mapping[str, Any]:
     __a			=					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
       __a	,       __a			=					common_inputs['input_ids'].shape
       # Not using the same length for past_key_values
       __a			=					seqlen + 2
       __a	,       __a			=					self.num_layers
       __a	,       __a			=					self.num_attention_heads
       __a			=					(
           batch,
           num_encoder_attention_heads,
           past_key_values_length,
           self._config.hidden_size // num_encoder_attention_heads,
       )

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







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase = -1      ,							UpperCamelCase = -1      ,							UpperCamelCase = False      ,							UpperCamelCase = None      ,							)				->    Mapping[str, Any]:
     __a			=					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
     __a			=					tokenizer.num_special_tokens_to_add(UpperCamelCase      )
     __a			=					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
     __a			=					[' '.join([tokenizer.unk_token]      ) * seq_length] * batch_size
     __a			=					dict(tokenizer(UpperCamelCase      ,							return_tensors=UpperCamelCase      )      )
     return common_inputs







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase = -1      ,							UpperCamelCase = -1      ,							UpperCamelCase = False      ,							UpperCamelCase = None      ,							)				->    Mapping[str, Any]:




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

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

     return common_inputs







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      )				->    int:




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







   @property
   def 		UpperCamelCase__			(	self      )				->    float:
     return 1e-4






'''simple docstring'''


import torch

from diffusers import StableDiffusionPipeline


UpperCAmelCase_ 		=      "path-to-your-trained-model"
UpperCAmelCase_ 		=      StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to("cuda")

UpperCAmelCase_ 		=      "A photo of sks dog in a bucket"
UpperCAmelCase_ 		=      pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]

image.save("dog-bucket.png")



