
"""simple docstring"""




import argparse
from pathlib import Path

import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version

from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse("""0.12.2"""):
    raise Exception("""requires fairseq >= 0.12.2""")
if version.parse(fairseq.__version__) > version.parse("""2"""):
    raise Exception("""requires fairseq < v2""")

logging.set_verbosity_info()
A__      :	Union[str, Any]          =    logging.get_logger(__name__)

A__      :	int          =    """Hello, World!"""
A__      :	Any          =    """en_XX"""



def 							a__ (				lowerCAmelCase	:       str       ,    lowerCAmelCase	:       str       ,    lowerCAmelCase	:       bool		):






     '''simple docstring'''
     UpperCAmelCase__							: Any 							=						Path("data_bin"		)
     UpperCAmelCase__							: Optional[Any] 							=						FairseqXmodModel.from_pretrained(
         model_name_or_path=str(Path(lowerCAmelCase		).parent		)       ,    checkpoint_file=Path(lowerCAmelCase		).name       ,    _name="xmod_base"       ,    arch="xmod_base"       ,    task="multilingual_masked_lm"       ,    data_name_or_path=str(lowerCAmelCase		)       ,    bpe="sentencepiece"       ,    sentencepiece_model=str(Path(lowerCAmelCase		).parent / "sentencepiece.bpe.model"		)       ,    src_dict=str(data_dir / "dict.txt"		)       ,    )
     xmod.eval()  # disable dropout
     print(lowerCAmelCase		)

     UpperCAmelCase__							: Dict 							=						xmod.model.encoder.sentence_encoder
     UpperCAmelCase__							: int 							=						XmodConfig(
         vocab_size=xmod_sent_encoder.embed_tokens.num_embeddings       ,    hidden_size=xmod.cfg.model.encoder_embed_dim       ,    num_hidden_layers=xmod.cfg.model.encoder_layers       ,    num_attention_heads=xmod.cfg.model.encoder_attention_heads       ,    intermediate_size=xmod.cfg.model.encoder_ffn_embed_dim       ,    max_position_embeddings=514       ,    type_vocab_size=1       ,    layer_norm_eps=1E-5       ,    pre_norm=xmod.cfg.model.encoder_normalize_before       ,    adapter_reduction_factor=getattr(xmod.cfg.model       ,    "bottleneck"       ,    2		)       ,    adapter_layer_norm=xmod.cfg.model.adapter_layer_norm       ,    adapter_reuse_layer_norm=xmod.cfg.model.adapter_reuse_layer_norm       ,    ln_before_adapter=xmod.cfg.model.ln_before_adapter       ,    languages=xmod.cfg.model.languages       ,    )
     if classification_head:
          UpperCAmelCase__							: str 							=						xmod.model.classification_heads["mnli"].out_proj.weight.shape[0]

     print("Our X-MOD config:"       ,    lowerCAmelCase		)

     UpperCAmelCase__							: Optional[int] 							=						XmodForSequenceClassification(lowerCAmelCase		) if classification_head else XmodForMaskedLM(lowerCAmelCase		)
     model.eval()

     # Now let's copy all the weights.
     # Embeddings
     UpperCAmelCase__							: List[Any] 							=						xmod_sent_encoder.embed_tokens.weight
     UpperCAmelCase__							: Optional[int] 							=						xmod_sent_encoder.embed_positions.weight
     UpperCAmelCase__							: Union[str, Any] 							=						torch.zeros_like(
         model.roberta.embeddings.token_type_embeddings.weight		)  # just zero them out b/c xmod doesn't use them.

     UpperCAmelCase__							: Any 							=						xmod_sent_encoder.layernorm_embedding.weight
     UpperCAmelCase__							: Tuple 							=						xmod_sent_encoder.layernorm_embedding.bias

     for i in range(config.num_hidden_layers		):
          # Encoder: start of layer
          UpperCAmelCase__							: int 							=						model.roberta.encoder.layer[i]
          UpperCAmelCase__							: Tuple 							=						xmod_sent_encoder.layers[i]

          # self attention
          UpperCAmelCase__							: Union[str, Any] 							=						layer.attention.self
          if not (
              xmod_layer.self_attn.k_proj.weight.data.shape
              == xmod_layer.self_attn.q_proj.weight.data.shape
              == xmod_layer.self_attn.v_proj.weight.data.shape
              == torch.Size((config.hidden_size, config.hidden_size)		)
          ):
               raise AssertionError("Dimensions of self-attention weights do not match."		)

          UpperCAmelCase__							: Any 							=						xmod_layer.self_attn.q_proj.weight
          UpperCAmelCase__							: Tuple 							=						xmod_layer.self_attn.q_proj.bias
          UpperCAmelCase__							: List[Any] 							=						xmod_layer.self_attn.k_proj.weight
          UpperCAmelCase__							: Any 							=						xmod_layer.self_attn.k_proj.bias
          UpperCAmelCase__							: List[Any] 							=						xmod_layer.self_attn.v_proj.weight
          UpperCAmelCase__							: List[Any] 							=						xmod_layer.self_attn.v_proj.bias

          # self-attention output
          UpperCAmelCase__							: Optional[Any] 							=						layer.attention.output
          if self_output.dense.weight.shape != xmod_layer.self_attn.out_proj.weight.shape:
               raise AssertionError("Dimensions of self-attention output weights do not match."		)
          UpperCAmelCase__							: List[Any] 							=						xmod_layer.self_attn.out_proj.weight
          UpperCAmelCase__							: Any 							=						xmod_layer.self_attn.out_proj.bias
          UpperCAmelCase__							: Union[str, Any] 							=						xmod_layer.self_attn_layer_norm.weight
          UpperCAmelCase__							: Optional[Any] 							=						xmod_layer.self_attn_layer_norm.bias

          # intermediate
          UpperCAmelCase__							: List[str] 							=						layer.intermediate
          if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
               raise AssertionError("Dimensions of intermediate weights do not match."		)
          UpperCAmelCase__							: Optional[int] 							=						xmod_layer.fca.weight
          UpperCAmelCase__							: str 							=						xmod_layer.fca.bias

          # output
          UpperCAmelCase__							: Union[str, Any] 							=						layer.output
          if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
               raise AssertionError("Dimensions of feed-forward weights do not match."		)
          UpperCAmelCase__							: str 							=						xmod_layer.fca.weight
          UpperCAmelCase__							: List[Any] 							=						xmod_layer.fca.bias
          UpperCAmelCase__							: Any 							=						xmod_layer.final_layer_norm.weight
          UpperCAmelCase__							: Tuple 							=						xmod_layer.final_layer_norm.bias
          if bert_output.adapter_layer_norm is not None:
               UpperCAmelCase__							: List[str] 							=						xmod_layer.adapter_layer_norm.weight
               UpperCAmelCase__							: Any 							=						xmod_layer.adapter_layer_norm.bias

          if sorted(bert_output.adapter_modules.keys()		) != sorted(xmod_layer.adapter_modules.keys()		):
               raise AssertionError("Lists of language adapters do not match."		)
          for lang_code, adapter in xmod_layer.adapter_modules.items():
               UpperCAmelCase__							: Any 							=						bert_output.adapter_modules[lang_code]
               UpperCAmelCase__							: Dict 							=						xmod_layer.adapter_modules[lang_code]
               UpperCAmelCase__							: Optional[Any] 							=						from_adapter.fca.weight
               UpperCAmelCase__							: Dict 							=						from_adapter.fca.bias
               UpperCAmelCase__							: Optional[int] 							=						from_adapter.fca.weight
               UpperCAmelCase__							: str 							=						from_adapter.fca.bias

          # end of layer

     if xmod_sent_encoder.layer_norm is not None:
          UpperCAmelCase__							: str 							=						xmod_sent_encoder.layer_norm.weight
          UpperCAmelCase__							: List[Any] 							=						xmod_sent_encoder.layer_norm.bias

     if classification_head:
          UpperCAmelCase__							: Dict 							=						xmod.model.classification_heads["mnli"].dense.weight
          UpperCAmelCase__							: Optional[int] 							=						xmod.model.classification_heads["mnli"].dense.bias
          UpperCAmelCase__							: Optional[Any] 							=						xmod.model.classification_heads["mnli"].out_proj.weight
          UpperCAmelCase__							: Optional[int] 							=						xmod.model.classification_heads["mnli"].out_proj.bias
     else:
          # LM Head
          UpperCAmelCase__							: List[Any] 							=						xmod.model.encoder.lm_head.dense.weight
          UpperCAmelCase__							: Dict 							=						xmod.model.encoder.lm_head.dense.bias
          UpperCAmelCase__							: Union[str, Any] 							=						xmod.model.encoder.lm_head.layer_norm.weight
          UpperCAmelCase__							: Dict 							=						xmod.model.encoder.lm_head.layer_norm.bias
          UpperCAmelCase__							: Dict 							=						xmod.model.encoder.lm_head.weight
          UpperCAmelCase__							: Any 							=						xmod.model.encoder.lm_head.bias

     # Let's check that we get the same results.
     UpperCAmelCase__							: Dict 							=						xmod.encode(lowerCAmelCase		).unsqueeze(0		)  # batch of size 1
     model.roberta.set_default_language(lowerCAmelCase		)

     UpperCAmelCase__							: Dict 							=						model(lowerCAmelCase		)[0]
     if classification_head:
          UpperCAmelCase__							: Any 							=						xmod.model.classification_heads["mnli"](xmod.extract_features(lowerCAmelCase		)		)
     else:
          UpperCAmelCase__							: Dict 							=						xmod.model(lowerCAmelCase       ,    lang_id=[SAMPLE_LANGUAGE]		)[0]
     print(our_output.shape       ,    their_output.shape		)
     UpperCAmelCase__							: str 							=						torch.max(torch.abs(our_output - their_output		)		).item()
     print(F"max_absolute_diff = {max_absolute_diff}"		)  # ~ 1e-7
     UpperCAmelCase__							: Optional[int] 							=						torch.allclose(lowerCAmelCase       ,    lowerCAmelCase       ,    atol=1E-3		)
     print("Do both models output the same tensors?"       ,    "🔥" if success else "💩"		)
     if not success:
          raise Exception("Something went wRoNg"		)

     Path(lowerCAmelCase		).mkdir(parents=lowerCAmelCase       ,    exist_ok=lowerCAmelCase		)
     print(F"Saving model to {pytorch_dump_folder_path}"		)
     model.save_pretrained(lowerCAmelCase		)


if __name__ == "__main__":
    A__      :	int          =    argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        """--xmod_checkpoint_path""", default=None, type=str, required=True, help="""Path the official PyTorch dump."""
    )
    parser.add_argument(
        """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
    )
    parser.add_argument(
        """--classification_head""", action="""store_true""", help="""Whether to convert a final classification head."""
    )
    A__      :	int          =    parser.parse_args()
    convert_xmod_checkpoint_to_pytorch(
        args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
    )





"""simple docstring"""




import unittest

import numpy as np

from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
    import onnxruntime as ort







class   _lowercase						(       lowerCAmelCase_	,   unittest.TestCase				):


     '''simple docstring'''





     pass







@nightly
@require_onnxruntime
@require_torch_gpu
class   _lowercase						(       unittest.TestCase				):


     '''simple docstring'''





     @property
     def  lowerCAmelCase__       (      self  )->					int:
          return (
              "CUDAExecutionProvider",
              {
                  "gpu_mem_limit": "15000000000",  # 15GB
                  "arena_extend_strategy": "kSameAsRequested",
              },
          )



     @property
     def  lowerCAmelCase__       (      self  )->					Any:
          UpperCAmelCase__							: Tuple 							=						ort.SessionOptions()
          UpperCAmelCase__							: List[str] 							=						False
          return options



     def  lowerCAmelCase__       (      self  )->					List[str]:
          UpperCAmelCase__							: Union[str, Any] 							=						load_image(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
              "/in_paint/overture-creations-5sI6fQgYIuo.png"  )
          UpperCAmelCase__							: int 							=						load_image(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
              "/in_paint/overture-creations-5sI6fQgYIuo_mask.png"  )
          UpperCAmelCase__							: str 							=						OnnxStableDiffusionInpaintPipeline.from_pretrained(
              "runwayml/stable-diffusion-inpainting" ,			revision="onnx" ,			safety_checker=__UpperCamelCase ,			feature_extractor=__UpperCamelCase ,			provider=self.gpu_provider ,			sess_options=self.gpu_options ,			)
          pipe.set_progress_bar_config(disable=__UpperCamelCase  )

          UpperCAmelCase__							: int 							=						"A red cat sitting on a park bench"

          UpperCAmelCase__							: Tuple 							=						np.random.RandomState(0  )
          UpperCAmelCase__							: Any 							=						pipe(
              prompt=__UpperCamelCase ,			image=__UpperCamelCase ,			mask_image=__UpperCamelCase ,			guidance_scale=7.5 ,			num_inference_steps=10 ,			generator=__UpperCamelCase ,			output_type="np" ,			)
          UpperCAmelCase__							: Tuple 							=						output.images
          UpperCAmelCase__							: Dict 							=						images[0, 2_55:2_58, 2_55:2_58, -1]

          assert images.shape == (1, 5_12, 5_12, 3)
          UpperCAmelCase__							: Union[str, Any] 							=						np.array([0.2514, 0.3007, 0.3517, 0.1790, 0.2382, 0.3167, 0.1944, 0.2273, 0.2464]  )

          assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1E-3



     def  lowerCAmelCase__       (      self  )->					Tuple:
          UpperCAmelCase__							: Tuple 							=						load_image(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
              "/in_paint/overture-creations-5sI6fQgYIuo.png"  )
          UpperCAmelCase__							: Tuple 							=						load_image(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
              "/in_paint/overture-creations-5sI6fQgYIuo_mask.png"  )
          UpperCAmelCase__							: Optional[Any] 							=						LMSDiscreteScheduler.from_pretrained(
              "runwayml/stable-diffusion-inpainting" ,			subfolder="scheduler" ,			revision="onnx"  )
          UpperCAmelCase__							: Optional[Any] 							=						OnnxStableDiffusionInpaintPipeline.from_pretrained(
              "runwayml/stable-diffusion-inpainting" ,			revision="onnx" ,			scheduler=__UpperCamelCase ,			safety_checker=__UpperCamelCase ,			feature_extractor=__UpperCamelCase ,			provider=self.gpu_provider ,			sess_options=self.gpu_options ,			)
          pipe.set_progress_bar_config(disable=__UpperCamelCase  )

          UpperCAmelCase__							: int 							=						"A red cat sitting on a park bench"

          UpperCAmelCase__							: List[str] 							=						np.random.RandomState(0  )
          UpperCAmelCase__							: str 							=						pipe(
              prompt=__UpperCamelCase ,			image=__UpperCamelCase ,			mask_image=__UpperCamelCase ,			guidance_scale=7.5 ,			num_inference_steps=20 ,			generator=__UpperCamelCase ,			output_type="np" ,			)
          UpperCAmelCase__							: List[str] 							=						output.images
          UpperCAmelCase__							: List[Any] 							=						images[0, 2_55:2_58, 2_55:2_58, -1]

          assert images.shape == (1, 5_12, 5_12, 3)
          UpperCAmelCase__							: int 							=						np.array([0.0086, 0.0077, 0.0083, 0.0093, 0.0107, 0.0139, 0.0094, 0.0097, 0.0125]  )

          assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1E-3



