





'''simple docstring'''



import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    PNDMScheduler,
    StableDiffusionLDMaDPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS


enable_full_determinism()


class       __SCREAMING_SNAKE_CASE	(						unittest.TestCase			):



     '''simple docstring'''






     UpperCAmelCase__       :						Optional[int]      =   StableDiffusionLDMaDPipeline
     UpperCAmelCase__       :						List[Any]      =   TEXT_TO_IMAGE_PARAMS
     UpperCAmelCase__       :						Union[str, Any]      =   TEXT_TO_IMAGE_BATCH_PARAMS
     UpperCAmelCase__       :						List[Any]      =   TEXT_TO_IMAGE_IMAGE_PARAMS

     def 	UpperCamelCase(					self	):
       torch.manual_seed(0	)
       _snake_case     		=				UNetaDConditionModel(
           block_out_channels=(32, 64)						,   layers_per_block=2						,   sample_size=32						,   in_channels=4						,   out_channels=4						,   down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")						,   up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")						,   cross_attention_dim=32						,   )
       _snake_case     		=				DDIMScheduler(
           beta_start=0.00085						,   beta_end=0.012						,   beta_schedule="scaled_linear"						,   clip_sample=lowerCamelCase						,   set_alpha_to_one=lowerCamelCase						,   )
       torch.manual_seed(0	)
       _snake_case     		=				AutoencoderKL(
           block_out_channels=[32, 64]						,   in_channels=6						,   out_channels=6						,   down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]						,   up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]						,   latent_channels=4						,   )
       torch.manual_seed(0	)
       _snake_case     		=				CLIPTextConfig(
           bos_token_id=0						,   eos_token_id=2						,   hidden_size=32						,   intermediate_size=37						,   layer_norm_eps=1e-05						,   num_attention_heads=4						,   num_hidden_layers=5						,   pad_token_id=1						,   vocab_size=1_000						,   )
       _snake_case     		=				CLIPTextModel(lowerCamelCase	)
       _snake_case     		=				CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"	)

       _snake_case     		=				{
           "unet": unet,
           "scheduler": scheduler,
           "vae": vae,
           "text_encoder": text_encoder,
           "tokenizer": tokenizer,
           "safety_checker": None,
           "feature_extractor": None,
       }
       return components

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase=0	):
       if str(lowerCamelCase	).startswith("mps"	):
         _snake_case     		=				torch.manual_seed(lowerCamelCase	)
       else:
         _snake_case     		=				torch.Generator(device=lowerCamelCase	).manual_seed(lowerCamelCase	)
       _snake_case     		=				{
           "prompt": "A painting of a squirrel eating a burger",
           "generator": generator,
           "num_inference_steps": 2,
           "guidance_scale": 6.0,
           "output_type": "numpy",
       }
       return inputs

     def 	UpperCamelCase(					self	):
       _snake_case     		=				"cpu"  # ensure determinism for the device-dependent torch.Generator

       _snake_case     		=				self.get_dummy_components()
       _snake_case     		=				StableDiffusionLDMaDPipeline(**lowerCamelCase	)
       _snake_case     		=				ldmad_pipe.to(lowerCamelCase	)
       ldmad_pipe.set_progress_bar_config(disable=lowerCamelCase	)

       _snake_case     		=				self.get_dummy_inputs(lowerCamelCase	)
       _snake_case     		=				ldmad_pipe(**lowerCamelCase	)
       _snake_case		,		_snake_case     		=				output.rgb, output.depth

       _snake_case     		=				rgb[0, -3:, -3:, -1]
       _snake_case     		=				depth[0, -3:, -1]

       assert rgb.shape == (1, 64, 64, 3)
       assert depth.shape == (1, 64, 64)

       _snake_case     		=				np.array(
           [0.37338176, 0.70247, 0.74203193, 0.51643604, 0.58256793, 0.60932136, 0.4181095, 0.48355877, 0.46535262]	)
       _snake_case     		=				np.array([103.46727, 85.812004, 87.849236]	)

       assert np.abs(image_slice_rgb.flatten() - expected_slice_rgb	).max() < 1e-2
       assert np.abs(image_slice_depth.flatten() - expected_slice_depth	).max() < 1e-2

     def 	UpperCamelCase(					self	):
       _snake_case     		=				self.get_dummy_components()
       _snake_case     		=				StableDiffusionLDMaDPipeline(**lowerCamelCase	)
       _snake_case     		=				ldmad_pipe.to(lowerCamelCase	)
       ldmad_pipe.set_progress_bar_config(disable=lowerCamelCase	)

       _snake_case     		=				self.get_dummy_inputs(lowerCamelCase	)
       _snake_case     		=				3 * [inputs["prompt"]]

       # forward
       _snake_case     		=				ldmad_pipe(**lowerCamelCase	)
       _snake_case		,		_snake_case     		=				output.rgb, output.depth
       _snake_case     		=				rgb_slice_a[0, -3:, -3:, -1]
       _snake_case     		=				depth_slice_a[0, -3:, -1]

       _snake_case     		=				self.get_dummy_inputs(lowerCamelCase	)
       _snake_case     		=				3 * [inputs.pop("prompt"	)]

       _snake_case     		=				ldmad_pipe.tokenizer(
           lowerCamelCase						,   padding="max_length"						,   max_length=ldmad_pipe.tokenizer.model_max_length						,   truncation=lowerCamelCase						,   return_tensors="pt"						,   )
       _snake_case     		=				text_inputs["input_ids"].to(lowerCamelCase	)

       _snake_case     		=				ldmad_pipe.text_encoder(lowerCamelCase	)[0]

       _snake_case     		=				prompt_embeds

       # forward
       _snake_case     		=				ldmad_pipe(**lowerCamelCase	)
       _snake_case		,		_snake_case     		=				output.rgb, output.depth
       _snake_case     		=				rgb_slice_a[0, -3:, -3:, -1]
       _snake_case     		=				depth_slice_a[0, -3:, -1]

       assert np.abs(rgb_slice_a.flatten() - rgb_slice_a.flatten()	).max() < 1e-4
       assert np.abs(depth_slice_a.flatten() - depth_slice_a.flatten()	).max() < 1e-4






     def 	UpperCamelCase(					self	):
       _snake_case     		=				"cpu"  # ensure determinism for the device-dependent torch.Generator
       _snake_case     		=				self.get_dummy_components()
       _snake_case     		=				PNDMScheduler(skip_prk_steps=lowerCamelCase	)
       _snake_case     		=				StableDiffusionLDMaDPipeline(**lowerCamelCase	)
       _snake_case     		=				ldmad_pipe.to(lowerCamelCase	)
       ldmad_pipe.set_progress_bar_config(disable=lowerCamelCase	)

       _snake_case     		=				self.get_dummy_inputs(lowerCamelCase	)
       _snake_case     		=				"french fries"
       _snake_case     		=				ldmad_pipe(**lowerCamelCase						,   negative_prompt=lowerCamelCase	)

       _snake_case		,		_snake_case     		=				output.rgb, output.depth
       _snake_case     		=				rgb[0, -3:, -3:, -1]
       _snake_case     		=				depth[0, -3:, -1]

       assert rgb.shape == (1, 64, 64, 3)
       assert depth.shape == (1, 64, 64)

       _snake_case     		=				np.array(
           [0.37044, 0.71811503, 0.7223251, 0.48603675, 0.5638391, 0.6364948, 0.42833704, 0.4901315, 0.47926217]	)
       _snake_case     		=				np.array([107.84738, 84.62802, 89.962135]	)
       assert np.abs(rgb_slice.flatten() - expected_slice_rgb	).max() < 1e-2
       assert np.abs(depth_slice.flatten() - expected_slice_depth	).max() < 1e-2







@slow
@require_torch_gpu
class       __SCREAMING_SNAKE_CASE	(						unittest.TestCase			):



     '''simple docstring'''






     def 	UpperCamelCase(					self	):
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase="cpu"						,   lowerCamelCase=torch.floataa						,   lowerCamelCase=0	):
       _snake_case     		=				torch.Generator(device=lowerCamelCase	).manual_seed(lowerCamelCase	)
       _snake_case     		=				np.random.RandomState(lowerCamelCase	).standard_normal((1, 4, 64, 64)	)
       _snake_case     		=				torch.from_numpy(lowerCamelCase	).to(device=lowerCamelCase						,   dtype=lowerCamelCase	)
       _snake_case     		=				{
           "prompt": "a photograph of an astronaut riding a horse",
           "latents": latents,
           "generator": generator,
           "num_inference_steps": 3,
           "guidance_scale": 7.5,
           "output_type": "numpy",
       }
       return inputs






     def 	UpperCamelCase(					self	):
       _snake_case     		=				StableDiffusionLDMaDPipeline.from_pretrained("Intel/ldm3d"	)
       _snake_case     		=				ldmad_pipe.to(lowerCamelCase	)
       ldmad_pipe.set_progress_bar_config(disable=lowerCamelCase	)

       _snake_case     		=				self.get_inputs(lowerCamelCase	)
       _snake_case     		=				ldmad_pipe(**lowerCamelCase	)
       _snake_case		,		_snake_case     		=				output.rgb, output.depth
       _snake_case     		=				rgb[0, -3:, -3:, -1].flatten()
       _snake_case     		=				rgb[0, -3:, -1].flatten()

       assert rgb.shape == (1, 512, 512, 3)
       assert depth.shape == (1, 512, 512)

       _snake_case     		=				np.array(
           [0.53805465, 0.56707305, 0.5486515, 0.57012236, 0.5814511, 0.56253487, 0.54843014, 0.55092263, 0.6459706]	)
       _snake_case     		=				np.array(
           [0.9263781, 0.6678672, 0.5486515, 0.92202145, 0.67831135, 0.56253487, 0.9241694, 0.7551478, 0.6459706]	)
       assert np.abs(rgb_slice - expected_slice_rgb	).max() < 3e-3
       assert np.abs(depth_slice - expected_slice_depth	).max() < 3e-3







@nightly
@require_torch_gpu
class       __SCREAMING_SNAKE_CASE	(						unittest.TestCase			):



     '''simple docstring'''






     def 	UpperCamelCase(					self	):
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase="cpu"						,   lowerCamelCase=torch.floataa						,   lowerCamelCase=0	):
       _snake_case     		=				torch.Generator(device=lowerCamelCase	).manual_seed(lowerCamelCase	)
       _snake_case     		=				np.random.RandomState(lowerCamelCase	).standard_normal((1, 4, 64, 64)	)
       _snake_case     		=				torch.from_numpy(lowerCamelCase	).to(device=lowerCamelCase						,   dtype=lowerCamelCase	)
       _snake_case     		=				{
           "prompt": "a photograph of an astronaut riding a horse",
           "latents": latents,
           "generator": generator,
           "num_inference_steps": 50,
           "guidance_scale": 7.5,
           "output_type": "numpy",
       }
       return inputs

     def 	UpperCamelCase(					self	):
       _snake_case     		=				StableDiffusionLDMaDPipeline.from_pretrained("Intel/ldm3d"	).to(lowerCamelCase	)
       ldmad_pipe.set_progress_bar_config(disable=lowerCamelCase	)

       _snake_case     		=				self.get_inputs(lowerCamelCase	)
       _snake_case     		=				ldmad_pipe(**lowerCamelCase	)
       _snake_case		,		_snake_case     		=				output.rgb, output.depth

       _snake_case     		=				0.495586
       _snake_case     		=				0.33795515
       _snake_case     		=				112.48518
       _snake_case     		=				98.489746
       assert np.abs(expected_rgb_mean - rgb.mean()	) < 1e-3
       assert np.abs(expected_rgb_std - rgb.std()	) < 1e-3
       assert np.abs(expected_depth_mean - depth.mean()	) < 1e-3
       assert np.abs(expected_depth_std - depth.std()	) < 1e-3






     def 	UpperCamelCase(					self	):
       _snake_case     		=				StableDiffusionLDMaDPipeline.from_pretrained("Intel/ldm3d-4c"	).to(lowerCamelCase	)
       ldmad_pipe.set_progress_bar_config(disable=lowerCamelCase	)

       _snake_case     		=				self.get_inputs(lowerCamelCase	)
       _snake_case     		=				ldmad_pipe(**lowerCamelCase	)
       _snake_case		,		_snake_case     		=				output.rgb, output.depth

       _snake_case     		=				0.4194127
       _snake_case     		=				0.35375586
       _snake_case     		=				0.5638502
       _snake_case     		=				0.34686103

       assert rgb.shape == (1, 512, 512, 3)
       assert depth.shape == (1, 512, 512, 1)
       assert np.abs(expected_rgb_mean - rgb.mean()	) < 1e-3
       assert np.abs(expected_rgb_std - rgb.std()	) < 1e-3
       assert np.abs(expected_depth_mean - depth.mean()	) < 1e-3
       assert np.abs(expected_depth_std - depth.std()	) < 1e-3









'''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()
__magic_name__   :						Union[str, Any]    =			logging.get_logger(__name__)

__magic_name__   :						Tuple    =			"""Hello, World!"""
__magic_name__   :						Union[str, Any]    =			"""en_XX"""




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  _snake_case     		=				Path("data_bin"       )
  _snake_case     		=				FairseqXmodModel.from_pretrained(
      model_name_or_path=str(Path(SCREAMING_SNAKE_CASE__       ).parent       )					,	checkpoint_file=Path(SCREAMING_SNAKE_CASE__       ).name					,	_name="xmod_base"					,	arch="xmod_base"					,	task="multilingual_masked_lm"					,	data_name_or_path=str(SCREAMING_SNAKE_CASE__       )					,	bpe="sentencepiece"					,	sentencepiece_model=str(Path(SCREAMING_SNAKE_CASE__       ).parent / "sentencepiece.bpe.model"       )					,	src_dict=str(data_dir / "dict.txt"       )					,	)
  xmod.eval()  # disable dropout
  print(SCREAMING_SNAKE_CASE__       )

  _snake_case     		=				xmod.model.encoder.sentence_encoder
  _snake_case     		=				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=5_14					,	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:
    _snake_case     		=				xmod.model.classification_heads["mnli"].out_proj.weight.shape[0]

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

  _snake_case     		=				XmodForSequenceClassification(SCREAMING_SNAKE_CASE__       ) if classification_head else XmodForMaskedLM(SCREAMING_SNAKE_CASE__       )
  model.eval()

  # Now let's copy all the weights.
  # Embeddings
  _snake_case     		=				xmod_sent_encoder.embed_tokens.weight
  _snake_case     		=				xmod_sent_encoder.embed_positions.weight
  _snake_case     		=				torch.zeros_like(
      model.roberta.embeddings.token_type_embeddings.weight       )  # just zero them out b/c xmod doesn't use them.

  _snake_case     		=				xmod_sent_encoder.layernorm_embedding.weight
  _snake_case     		=				xmod_sent_encoder.layernorm_embedding.bias

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

    # self attention
    _snake_case     		=				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."       )

    _snake_case     		=				xmod_layer.self_attn.q_proj.weight
    _snake_case     		=				xmod_layer.self_attn.q_proj.bias
    _snake_case     		=				xmod_layer.self_attn.k_proj.weight
    _snake_case     		=				xmod_layer.self_attn.k_proj.bias
    _snake_case     		=				xmod_layer.self_attn.v_proj.weight
    _snake_case     		=				xmod_layer.self_attn.v_proj.bias

    # self-attention output
    _snake_case     		=				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."       )
    _snake_case     		=				xmod_layer.self_attn.out_proj.weight
    _snake_case     		=				xmod_layer.self_attn.out_proj.bias
    _snake_case     		=				xmod_layer.self_attn_layer_norm.weight
    _snake_case     		=				xmod_layer.self_attn_layer_norm.bias

    # intermediate
    _snake_case     		=				layer.intermediate
    if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
      raise AssertionError("Dimensions of intermediate weights do not match."       )
    _snake_case     		=				xmod_layer.fca.weight
    _snake_case     		=				xmod_layer.fca.bias

    # output
    _snake_case     		=				layer.output
    if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
      raise AssertionError("Dimensions of feed-forward weights do not match."       )
    _snake_case     		=				xmod_layer.fca.weight
    _snake_case     		=				xmod_layer.fca.bias
    _snake_case     		=				xmod_layer.final_layer_norm.weight
    _snake_case     		=				xmod_layer.final_layer_norm.bias
    if bert_output.adapter_layer_norm is not None:
      _snake_case     		=				xmod_layer.adapter_layer_norm.weight
      _snake_case     		=				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():
      _snake_case     		=				bert_output.adapter_modules[lang_code]
      _snake_case     		=				xmod_layer.adapter_modules[lang_code]
      _snake_case     		=				from_adapter.fca.weight
      _snake_case     		=				from_adapter.fca.bias
      _snake_case     		=				from_adapter.fca.weight
      _snake_case     		=				from_adapter.fca.bias

    # end of layer

  if xmod_sent_encoder.layer_norm is not None:
    _snake_case     		=				xmod_sent_encoder.layer_norm.weight
    _snake_case     		=				xmod_sent_encoder.layer_norm.bias

  if classification_head:
    _snake_case     		=				xmod.model.classification_heads["mnli"].dense.weight
    _snake_case     		=				xmod.model.classification_heads["mnli"].dense.bias
    _snake_case     		=				xmod.model.classification_heads["mnli"].out_proj.weight
    _snake_case     		=				xmod.model.classification_heads["mnli"].out_proj.bias
  else:
    # LM Head
    _snake_case     		=				xmod.model.encoder.lm_head.dense.weight
    _snake_case     		=				xmod.model.encoder.lm_head.dense.bias
    _snake_case     		=				xmod.model.encoder.lm_head.layer_norm.weight
    _snake_case     		=				xmod.model.encoder.lm_head.layer_norm.bias
    _snake_case     		=				xmod.model.encoder.lm_head.weight
    _snake_case     		=				xmod.model.encoder.lm_head.bias

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

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

  Path(SCREAMING_SNAKE_CASE__       ).mkdir(parents=SCREAMING_SNAKE_CASE__					,	exist_ok=SCREAMING_SNAKE_CASE__       )
  print(f'''Saving model to {pytorch_dump_folder_path}'''       )
  model.save_pretrained(SCREAMING_SNAKE_CASE__       )


if __name__ == "__main__":
 __magic_name__   :						str    =			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."""
 )
 __magic_name__   :						List[Any]    =			parser.parse_args()
 convert_xmod_checkpoint_to_pytorch(
     args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
 )


