




"""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()
_lowerCamelCase  :			Union[str, Any]        =       logging.get_logger(__name__)

_lowerCamelCase  :			List[str]        =       'Hello, World!'
_lowerCamelCase  :			str        =       'en_XX'

def 		lowercase_      (		_UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase	):




	"""simple docstring"""






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

	A_  :				Optional[int]	   =		xmod.model.encoder.sentence_encoder
	A_  :				Dict	   =		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:
		A_  :				List[str]	   =		xmod.model.classification_heads['''mnli'''].out_proj.weight.shape[0]

	print('''Our X-MOD config:'''  ,      _UpperCAmelCase	)

	A_  :				Any	   =		XmodForSequenceClassification(_UpperCAmelCase	) if classification_head else XmodForMaskedLM(_UpperCAmelCase	)
	model.eval()

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

	A_  :				Union[str, Any]	   =		xmod_sent_encoder.layernorm_embedding.weight
	A_  :				List[Any]	   =		xmod_sent_encoder.layernorm_embedding.bias

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

		# self attention
		A_  :				Optional[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.'''	)

		A_  :				Any	   =		xmod_layer.self_attn.q_proj.weight
		A_  :				Union[str, Any]	   =		xmod_layer.self_attn.q_proj.bias
		A_  :				Union[str, Any]	   =		xmod_layer.self_attn.k_proj.weight
		A_  :				List[str]	   =		xmod_layer.self_attn.k_proj.bias
		A_  :				Optional[Any]	   =		xmod_layer.self_attn.v_proj.weight
		A_  :				Any	   =		xmod_layer.self_attn.v_proj.bias

		# self-attention output
		A_  :				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.'''	)
		A_  :				Tuple	   =		xmod_layer.self_attn.out_proj.weight
		A_  :				List[str]	   =		xmod_layer.self_attn.out_proj.bias
		A_  :				List[Any]	   =		xmod_layer.self_attn_layer_norm.weight
		A_  :				Dict	   =		xmod_layer.self_attn_layer_norm.bias

		# intermediate
		A_  :				Union[str, Any]	   =		layer.intermediate
		if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
			raise AssertionError('''Dimensions of intermediate weights do not match.'''	)
		A_  :				Union[str, Any]	   =		xmod_layer.fca.weight
		A_  :				Any	   =		xmod_layer.fca.bias

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

		# end of layer

	if xmod_sent_encoder.layer_norm is not None:
		A_  :				Tuple	   =		xmod_sent_encoder.layer_norm.weight
		A_  :				Optional[Any]	   =		xmod_sent_encoder.layer_norm.bias

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

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

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

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


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











"""simple docstring"""

import gc
import random
import unittest

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

from diffusers import (
    AutoencoderKL,
    DiffusionPipeline,
    EulerDiscreteScheduler,
    StableDiffusionXLImgaImgPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()

class        lowercase      (    __UpperCAmelCase     ,	__UpperCAmelCase     ,	unittest.TestCase):
				__lowerCAmelCase    :    int         =   StableDiffusionXLImgaImgPipeline
				__lowerCAmelCase    :    List[Any]         =   TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""height""", """width"""}
				__lowerCAmelCase    :    Optional[int]         =   PipelineTesterMixin.required_optional_params - {"""latents"""}
				__lowerCAmelCase    :    Any         =   TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
				__lowerCAmelCase    :    Optional[Any]         =   IMAGE_TO_IMAGE_IMAGE_PARAMS
				__lowerCAmelCase    :    List[str]         =   IMAGE_TO_IMAGE_IMAGE_PARAMS







				def 					a_  (							self   :					Any       ):



					"""simple docstring"""

					torch.manual_seed(0       )
					A_  :				int	   =		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''')      ,     attention_head_dim=(2, 4)      ,     use_linear_projection=_lowerCamelCase      ,     addition_embed_type='''text_time'''      ,     addition_time_embed_dim=8      ,     transformer_layers_per_block=(1, 2)      ,     projection_class_embeddings_input_dim=80      ,     cross_attention_dim=64      ,     )
					A_  :				Tuple	   =		EulerDiscreteScheduler(
					    beta_start=0.00085      ,     beta_end=0.012      ,     steps_offset=1      ,     beta_schedule='''scaled_linear'''      ,     timestep_spacing='''leading'''      ,     )
					torch.manual_seed(0       )
					A_  :				Optional[Any]	   =		AutoencoderKL(
					    block_out_channels=[32, 64]      ,     in_channels=3      ,     out_channels=3      ,     down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']      ,     up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']      ,     latent_channels=4      ,     sample_size=1_28      ,     )
					torch.manual_seed(0       )
					A_  :				Dict	   =		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=10_00      ,     hidden_act='''gelu'''      ,     projection_dim=32      ,     )
					A_  :				Any	   =		CLIPTextModel(_lowerCamelCase       )
					A_  :				List[Any]	   =		CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''      ,     local_files_only=_lowerCamelCase       )

					A_  :				int	   =		CLIPTextModelWithProjection(_lowerCamelCase       )
					A_  :				Any	   =		CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''      ,     local_files_only=_lowerCamelCase       )

					A_  :				Union[str, Any]	   =		{
					    '''unet''': unet,
					    '''scheduler''': scheduler,
					    '''vae''': vae,
					    '''text_encoder''': text_encoder,
					    '''tokenizer''': tokenizer,
					    '''text_encoder_2''': text_encoder_a,
					    '''tokenizer_2''': tokenizer_a,
					    # "safety_checker": None,
					    # "feature_extractor": None,
					}
					return components







				def 					a_  (							self   :					Tuple      ,     _lowerCamelCase   :					int      ,     _lowerCamelCase   :					List[Any]=0       ):



					"""simple docstring"""

					A_  :				Dict	   =		floats_tensor((1, 3, 32, 32)      ,     rng=random.Random(_lowerCamelCase       )       ).to(_lowerCamelCase       )
					A_  :				Optional[Any]	   =		image / 2 + 0.5
					if str(_lowerCamelCase       ).startswith('''mps'''       ):
						A_  :				str	   =		torch.manual_seed(_lowerCamelCase       )
					else:
						A_  :				Any	   =		torch.Generator(device=_lowerCamelCase       ).manual_seed(_lowerCamelCase       )
					A_  :				Any	   =		{
					    '''prompt''': '''A painting of a squirrel eating a burger''',
					    '''image''': image,
					    '''generator''': generator,
					    '''num_inference_steps''': 2,
					    '''guidance_scale''': 5.0,
					    '''output_type''': '''numpy''',
					    '''strength''': 0.75,
					}
					return inputs







				def 					a_  (							self   :					List[Any]       ):



					"""simple docstring"""

					A_  :				List[str]	   =		'''cpu'''  # ensure determinism for the device-dependent torch.Generator
					A_  :				Dict	   =		self.get_dummy_components()
					A_  :				Dict	   =		StableDiffusionXLImgaImgPipeline(**_lowerCamelCase       )
					A_  :				int	   =		sd_pipe.to(_lowerCamelCase       )
					sd_pipe.set_progress_bar_config(disable=_lowerCamelCase       )

					A_  :				Union[str, Any]	   =		self.get_dummy_inputs(_lowerCamelCase       )
					A_  :				Tuple	   =		sd_pipe(**_lowerCamelCase       ).images
					A_  :				Dict	   =		image[0, -3:, -3:, -1]

					assert image.shape == (1, 32, 32, 3)

					A_  :				Union[str, Any]	   =		np.array([0.4656, 0.4840, 0.4439, 0.6698, 0.5574, 0.4524, 0.5799, 0.5943, 0.5165]       )

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







				def 					a_  (							self   :					Optional[int]       ):



					"""simple docstring"""

					super().test_attention_slicing_forward_pass(expected_max_diff=3E-3       )







				def 					a_  (							self   :					Dict       ):



					"""simple docstring"""

					super().test_inference_batch_single_identical(expected_max_diff=3E-3       )







				def 					a_  (							self   :					List[Any]       ):



					"""simple docstring"""

					pass







				def 					a_  (							self   :					List[str]       ):



					"""simple docstring"""

					A_  :				Optional[Any]	   =		self.get_dummy_components()
					A_  :				int	   =		StableDiffusionXLImgaImgPipeline(**_lowerCamelCase       )
					A_  :				Dict	   =		sd_pipe.to(_lowerCamelCase       )
					A_  :				List[Any]	   =		sd_pipe.to(_lowerCamelCase       )
					sd_pipe.set_progress_bar_config(disable=_lowerCamelCase       )

					# forward without prompt embeds
					A_  :				Any	   =		self.get_dummy_inputs(_lowerCamelCase       )
					A_  :				Union[str, Any]	   =		3 * ['''this is a negative prompt''']
					A_  :				Any	   =		negative_prompt
					A_  :				Any	   =		3 * [inputs['''prompt''']]

					A_  :				Union[str, Any]	   =		sd_pipe(**_lowerCamelCase       )
					A_  :				List[str]	   =		output.images[0, -3:, -3:, -1]

					# forward with prompt embeds
					A_  :				Optional[Any]	   =		self.get_dummy_inputs(_lowerCamelCase       )
					A_  :				str	   =		3 * ['''this is a negative prompt''']
					A_  :				List[str]	   =		3 * [inputs.pop('''prompt'''       )]

					(
					    (
					    A_
					)   , (
					    A_
					)   , (
					    A_
					)   , (
					    A_
					)   , 
					)  :				List[str]	   =		sd_pipe.encode_prompt(_lowerCamelCase      ,     negative_prompt=_lowerCamelCase       )

					A_  :				str	   =		sd_pipe(
					    **_lowerCamelCase      ,     prompt_embeds=_lowerCamelCase      ,     negative_prompt_embeds=_lowerCamelCase      ,     pooled_prompt_embeds=_lowerCamelCase      ,     negative_pooled_prompt_embeds=_lowerCamelCase      ,     )
					A_  :				Any	   =		output.images[0, -3:, -3:, -1]

					# make sure that it's equal
					assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()       ).max() < 1E-4

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







				def 					a_  (							self   :					Dict       ):



					"""simple docstring"""

					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()







				def 					a_  (							self   :					Tuple      ,     _lowerCamelCase   :					List[str]      ,     _lowerCamelCase   :					Union[str, Any]="cpu"      ,     _lowerCamelCase   :					Optional[Any]=torch.floataa      ,     _lowerCamelCase   :					Optional[int]=0       ):



					"""simple docstring"""

					A_  :				int	   =		torch.Generator(device=_lowerCamelCase       ).manual_seed(_lowerCamelCase       )
					A_  :				List[Any]	   =		np.random.RandomState(_lowerCamelCase       ).standard_normal((1, 4, 64, 64)       )
					A_  :				Union[str, Any]	   =		torch.from_numpy(_lowerCamelCase       ).to(device=_lowerCamelCase      ,     dtype=_lowerCamelCase       )
					A_  :				Optional[Any]	   =		{
					    '''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 					a_  (							self   :					Tuple       ):



					"""simple docstring"""

					A_  :				str	   =		DiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-base'''       )
					pipe.to(_lowerCamelCase       )
					pipe.set_progress_bar_config(disable=_lowerCamelCase       )

					A_  :				Optional[Any]	   =		self.get_inputs(_lowerCamelCase       )
					A_  :				Union[str, Any]	   =		pipe(**_lowerCamelCase       ).images
					A_  :				List[Any]	   =		image[0, -3:, -3:, -1].flatten()

					assert image.shape == (1, 5_12, 5_12, 3)
					A_  :				int	   =		np.array([0.49493, 0.47896, 0.40798, 0.54214, 0.53212, 0.48202, 0.47656, 0.46329, 0.48506]       )
					assert np.abs(image_slice - expected_slice       ).max() < 7E-3





