


import argparse
import os
import pathlib

import fairseq
import torch
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import (
    DataaVecTextConfig,
    DataaVecTextForMaskedLM,
    DataaVecTextForSequenceClassification,
    DataaVecTextModel,
)
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)

# IMPORTANT: In order for this script to run, please make sure to download the dictionary: `dict.txt` from wget https://dl.fbaipublicfiles.com/fairseq/models/roberta.large.tar.gz
# File copied from https://github.com/pytorch/fairseq/blob/main/examples/data2vec/models/data2vec_text.py
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse("""0.9.0"""):
						raise Exception("""requires fairseq >= 0.9.0""")


logging.set_verbosity_info()
_UpperCAmelCase      :	List[str]  =logging.get_logger(__name__)

_UpperCAmelCase      :	int  ="""Hello world! cécé herlolip"""



def       lowerCAmelCase			(		lowerCAmelCase_			, lowerCAmelCase_			, lowerCAmelCase_			)->			Any:
				lowerCAmelCase_					,	lowerCAmelCase_			:   Optional[int]     					=					os.path.split(lowerCAmelCase_			)
				lowerCAmelCase_			:   Any     					=					DataaVecTextModel.from_pretrained(
				    lowerCAmelCase_			, checkpoint_file=lowerCAmelCase_			)
				dataavec.eval()  # disable dropout
				lowerCAmelCase_			:   Optional[int]     					=					dataavec.models[0]
				lowerCAmelCase_			:   Tuple     					=					dataavec_model.encoder.sentence_encoder
				lowerCAmelCase_			:   int     					=					DataaVecTextConfig(
				    vocab_size=dataavec_sent_encoder.embed_tokens.num_embeddings			, hidden_size=dataavec_model.args.encoder_embed_dim			, num_hidden_layers=dataavec_model.args.encoder_layers			, num_attention_heads=dataavec_model.args.encoder_attention_heads			, intermediate_size=dataavec_model.args.encoder_ffn_embed_dim			, max_position_embeddings=514			, type_vocab_size=1			, layer_norm_eps=1e-5			, )
				if classification_head:
								lowerCAmelCase_			:   Any     					=					dataavec.model.classification_heads['''mnli'''].out_proj.weight.shape[0]
				print('''Our BERT config:'''			, lowerCAmelCase_			)

				lowerCAmelCase_			:   Optional[Any]     					=					DataaVecTextForSequenceClassification(lowerCAmelCase_			) if classification_head else DataaVecTextForMaskedLM(lowerCAmelCase_			)
				model.eval()

				# Now let's copy all the weights.
				# Embeddings
				lowerCAmelCase_			:   List[Any]     					=					dataavec_sent_encoder.embed_tokens.weight
				lowerCAmelCase_			:   int     					=					dataavec_sent_encoder.embed_positions.weight
				lowerCAmelCase_			:   Any     					=					torch.zeros_like(
				    model.dataavec_text.embeddings.token_type_embeddings.weight			)  # just zero them out b/c data2vec doesn't use them.
				lowerCAmelCase_			:   Dict     					=					dataavec_sent_encoder.layernorm_embedding.weight
				lowerCAmelCase_			:   str     					=					dataavec_sent_encoder.layernorm_embedding.bias

				for i in range(config.num_hidden_layers			):
								# Encoder: start of layer
								lowerCAmelCase_			:   BertLayer     					=					model.dataavec_text.encoder.layer[i]
								lowerCAmelCase_			:   TransformerSentenceEncoderLayer     					=					dataavec_sent_encoder.layers[i]

								# self attention
								lowerCAmelCase_			:   BertSelfAttention     					=					layer.attention.self
								assert dataavec_layer.self_attn.k_proj.weight.data.shape == torch.Size(
								    (config.hidden_size, config.hidden_size)			), (
								    "Shape for data2vec_layer.self_attn.k_proj.weight.data should be"
								    f""" {torch.Size((config.hidden_size, config.hidden_size)			)}"""
								)
								assert dataavec_layer.self_attn.q_proj.weight.data.shape == torch.Size(
								    (config.hidden_size, config.hidden_size)			), (
								    "Shape for data2vec_layer.self_attn.q_proj.weight.data should be"
								    f""" {torch.Size((config.hidden_size, config.hidden_size)			)}"""
								)
								assert dataavec_layer.self_attn.v_proj.weight.data.shape == torch.Size(
								    (config.hidden_size, config.hidden_size)			), (
								    "Shape for data2vec_layer.self_attn.v_proj.weight.data should be"
								    f""" {torch.Size((config.hidden_size, config.hidden_size)			)}"""
								)

								lowerCAmelCase_			:   List[Any]     					=					dataavec_layer.self_attn.q_proj.weight
								lowerCAmelCase_			:   Optional[Any]     					=					dataavec_layer.self_attn.q_proj.bias
								lowerCAmelCase_			:   Union[str, Any]     					=					dataavec_layer.self_attn.k_proj.weight
								lowerCAmelCase_			:   Optional[Any]     					=					dataavec_layer.self_attn.k_proj.bias
								lowerCAmelCase_			:   str     					=					dataavec_layer.self_attn.v_proj.weight
								lowerCAmelCase_			:   Optional[int]     					=					dataavec_layer.self_attn.v_proj.bias

								# self-attention output
								lowerCAmelCase_			:   BertSelfOutput     					=					layer.attention.output
								assert (
								    self_output.dense.weight.shape == dataavec_layer.self_attn.out_proj.weight.shape
								), f"""Shape for self_output.dense.weight should be {dataavec_layer.self_attn.out_proj.weight.shape}"""
								lowerCAmelCase_			:   str     					=					dataavec_layer.self_attn.out_proj.weight
								lowerCAmelCase_			:   Tuple     					=					dataavec_layer.self_attn.out_proj.bias
								lowerCAmelCase_			:   str     					=					dataavec_layer.self_attn_layer_norm.weight
								lowerCAmelCase_			:   List[str]     					=					dataavec_layer.self_attn_layer_norm.bias

								# intermediate
								lowerCAmelCase_			:   BertIntermediate     					=					layer.intermediate
								assert (
								    intermediate.dense.weight.shape == dataavec_layer.fca.weight.shape
								), f"""Shape for intermediate.dense.weight should be {dataavec_layer.fca.weight.shape}"""
								lowerCAmelCase_			:   str     					=					dataavec_layer.fca.weight
								lowerCAmelCase_			:   int     					=					dataavec_layer.fca.bias

								# output
								lowerCAmelCase_			:   BertOutput     					=					layer.output
								assert (
								    bert_output.dense.weight.shape == dataavec_layer.fca.weight.shape
								), f"""Shape for bert_output.dense.weight should be {dataavec_layer.fca.weight.shape}"""
								lowerCAmelCase_			:   Dict     					=					dataavec_layer.fca.weight
								lowerCAmelCase_			:   str     					=					dataavec_layer.fca.bias
								lowerCAmelCase_			:   Optional[Any]     					=					dataavec_layer.final_layer_norm.weight
								lowerCAmelCase_			:   Optional[Any]     					=					dataavec_layer.final_layer_norm.bias
								# end of layer

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

				# Let's check that we get the same results.
				lowerCAmelCase_			:   torch.Tensor     					=					dataavec.encode(lowerCAmelCase_			).unsqueeze(0			)  # batch of size 1

				lowerCAmelCase_			:   str     					=					model(lowerCAmelCase_			)[0]
				if classification_head:
								lowerCAmelCase_			:   Tuple     					=					dataavec.model.classification_heads['''mnli'''](dataavec.extract_features(lowerCAmelCase_			)			)
				else:
								lowerCAmelCase_			:   str     					=					dataavec_model(lowerCAmelCase_			)[0]
				print(our_output.shape			, their_output.shape			)
				lowerCAmelCase_			:   int     					=					torch.max(torch.abs(our_output - their_output			)			).item()
				print(f"""max_absolute_diff = {max_absolute_diff}"""			)  # ~ 1e-7
				lowerCAmelCase_			:   Dict     					=					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'''			)

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


if __name__ == "__main__":
						_UpperCAmelCase      :	Optional[int]  =argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    """--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."""
						)
						_UpperCAmelCase      :	Any  =parser.parse_args()
						convert_dataavec_checkpoint_to_pytorch(
						    args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
						)



import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DiffusionPipeline,
    EulerDiscreteScheduler,
    StableDiffusionXLPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import 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
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()







class 					snake_case__(				UpperCAmelCase__,    UpperCAmelCase__,    unittest.TestCase				):







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__   :	str       =				StableDiffusionXLPipeline
						SCREAMING_SNAKE_CASE__   :	Dict       =				TEXT_TO_IMAGE_PARAMS
						SCREAMING_SNAKE_CASE__   :	Optional[int]       =				TEXT_TO_IMAGE_BATCH_PARAMS
						SCREAMING_SNAKE_CASE__   :	Optional[Any]       =				TEXT_TO_IMAGE_IMAGE_PARAMS
						SCREAMING_SNAKE_CASE__   :	int       =				TEXT_TO_IMAGE_IMAGE_PARAMS




						def     lowercase_			(					self				)       ->     int:
										torch.manual_seed(0				)
										lowerCAmelCase_			:   Tuple     					=					UNetaDConditionModel(
										    block_out_channels=(3_2, 6_4) ,    layers_per_block=2 ,    sample_size=3_2 ,    in_channels=4 ,    out_channels=4 ,    down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''') ,    up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''') ,    attention_head_dim=(2, 4) ,    use_linear_projection=__lowercase ,    addition_embed_type='''text_time''' ,    addition_time_embed_dim=8 ,    transformer_layers_per_block=(1, 2) ,    projection_class_embeddings_input_dim=8_0 ,    cross_attention_dim=6_4 ,    )
										lowerCAmelCase_			:   Any     					=					EulerDiscreteScheduler(
										    beta_start=0.0_00_85 ,    beta_end=0.0_12 ,    steps_offset=1 ,    beta_schedule='''scaled_linear''' ,    timestep_spacing='''leading''' ,    )
										torch.manual_seed(0				)
										lowerCAmelCase_			:   Any     					=					AutoencoderKL(
										    block_out_channels=[3_2, 6_4] ,    in_channels=3 ,    out_channels=3 ,    down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''] ,    up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''] ,    latent_channels=4 ,    sample_size=1_2_8 ,    )
										torch.manual_seed(0				)
										lowerCAmelCase_			:   Dict     					=					CLIPTextConfig(
										    bos_token_id=0 ,    eos_token_id=2 ,    hidden_size=3_2 ,    intermediate_size=3_7 ,    layer_norm_eps=1e-05 ,    num_attention_heads=4 ,    num_hidden_layers=5 ,    pad_token_id=1 ,    vocab_size=1_0_0_0 ,    hidden_act='''gelu''' ,    projection_dim=3_2 ,    )
										lowerCAmelCase_			:   str     					=					CLIPTextModel(__lowercase				)
										lowerCAmelCase_			:   List[str]     					=					CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' ,    local_files_only=__lowercase				)

										lowerCAmelCase_			:   Tuple     					=					CLIPTextModelWithProjection(__lowercase				)
										lowerCAmelCase_			:   Optional[Any]     					=					CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' ,    local_files_only=__lowercase				)

										lowerCAmelCase_			:   int     					=					{
										    '''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     lowercase_			(					self ,    __lowercase ,    __lowercase=0				)       ->     Dict:
										if str(__lowercase				).startswith('''mps'''				):
														lowerCAmelCase_			:   Dict     					=					torch.manual_seed(__lowercase				)
										else:
														lowerCAmelCase_			:   Dict     					=					torch.Generator(device=__lowercase				).manual_seed(__lowercase				)
										lowerCAmelCase_			:   Dict     					=					{
										    '''prompt''': '''A painting of a squirrel eating a burger''',
										    '''generator''': generator,
										    '''num_inference_steps''': 2,
										    '''guidance_scale''': 5.0,
										    '''output_type''': '''numpy''',
										}
										return inputs




						def     lowercase_			(					self				)       ->     Optional[Any]:
										lowerCAmelCase_			:   Union[str, Any]     					=					'''cpu'''  # ensure determinism for the device-dependent torch.Generator
										lowerCAmelCase_			:   Any     					=					self.get_dummy_components()
										lowerCAmelCase_			:   Optional[int]     					=					StableDiffusionXLPipeline(**__lowercase				)
										lowerCAmelCase_			:   Optional[int]     					=					sd_pipe.to(__lowercase				)
										sd_pipe.set_progress_bar_config(disable=__lowercase				)

										lowerCAmelCase_			:   Dict     					=					self.get_dummy_inputs(__lowercase				)
										lowerCAmelCase_			:   str     					=					sd_pipe(**__lowercase				).images
										lowerCAmelCase_			:   Any     					=					image[0, -3:, -3:, -1]

										assert image.shape == (1, 6_4, 6_4, 3)
										lowerCAmelCase_			:   str     					=					np.array([0.58_73, 0.61_28, 0.47_97, 0.51_22, 0.56_74, 0.46_39, 0.52_27, 0.51_49, 0.47_47]				)

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




						def     lowercase_			(					self				)       ->     List[Any]:
										lowerCAmelCase_			:   Any     					=					self.get_dummy_components()
										lowerCAmelCase_			:   List[Any]     					=					StableDiffusionXLPipeline(**__lowercase				)
										lowerCAmelCase_			:   List[Any]     					=					sd_pipe.to(__lowercase				)
										lowerCAmelCase_			:   int     					=					sd_pipe.to(__lowercase				)
										sd_pipe.set_progress_bar_config(disable=__lowercase				)

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

										lowerCAmelCase_			:   str     					=					sd_pipe(**__lowercase				)
										lowerCAmelCase_			:   Any     					=					output.images[0, -3:, -3:, -1]

										# forward with prompt embeds
										lowerCAmelCase_			:   Tuple     					=					self.get_dummy_inputs(__lowercase				)
										lowerCAmelCase_			:   Dict     					=					3 * ['''this is a negative prompt''']
										lowerCAmelCase_			:   Dict     					=					3 * [inputs.pop('''prompt'''				)]

										(
										    (
										    lowerCAmelCase_
										)					,	(
										    lowerCAmelCase_
										)					,	(
										    lowerCAmelCase_
										)					,	(
										    lowerCAmelCase_
										)					,	
										)			:   str     					=					sd_pipe.encode_prompt(__lowercase ,    negative_prompt=__lowercase				)

										lowerCAmelCase_			:   List[Any]     					=					sd_pipe(
										    **__lowercase ,    prompt_embeds=__lowercase ,    negative_prompt_embeds=__lowercase ,    pooled_prompt_embeds=__lowercase ,    negative_pooled_prompt_embeds=__lowercase ,    )
										lowerCAmelCase_			:   int     					=					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




						def     lowercase_			(					self				)       ->     List[str]:
										super().test_attention_slicing_forward_pass(expected_max_diff=3e-3				)




						def     lowercase_			(					self				)       ->     Tuple:
										super().test_inference_batch_single_identical(expected_max_diff=3e-3				)







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







						'''simple docstring'''




						def     lowercase_			(					self				)       ->     int:
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()




						def     lowercase_			(					self ,    __lowercase ,    __lowercase="cpu" ,    __lowercase=torch.floataa ,    __lowercase=0				)       ->     Dict:
										lowerCAmelCase_			:   Any     					=					torch.Generator(device=__lowercase				).manual_seed(__lowercase				)
										lowerCAmelCase_			:   str     					=					np.random.RandomState(__lowercase				).standard_normal((1, 4, 6_4, 6_4)				)
										lowerCAmelCase_			:   List[str]     					=					torch.from_numpy(__lowercase				).to(device=__lowercase ,    dtype=__lowercase				)
										lowerCAmelCase_			:   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     lowercase_			(					self				)       ->     List[str]:
										lowerCAmelCase_			:   Optional[int]     					=					DiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-base'''				)
										pipe.to(__lowercase				)
										pipe.set_progress_bar_config(disable=__lowercase				)

										lowerCAmelCase_			:   Any     					=					self.get_inputs(__lowercase				)
										lowerCAmelCase_			:   List[str]     					=					pipe(**__lowercase				).images
										lowerCAmelCase_			:   Dict     					=					image[0, -3:, -3:, -1].flatten()

										assert image.shape == (1, 5_1_2, 5_1_2, 3)
										lowerCAmelCase_			:   Optional[int]     					=					np.array([0.4_94_93, 0.4_78_96, 0.4_07_98, 0.5_42_14, 0.5_32_12, 0.4_82_02, 0.4_76_56, 0.4_63_29, 0.4_85_06]				)
										assert np.abs(image_slice - expected_slice				).max() < 7e-3