
import argparse
import os
from pathlib import Path

import fairseq
import torch
from packaging import version
from torch import nn

from transformers import (
    BartConfig,
    BartForConditionalGeneration,
    BartForSequenceClassification,
    BartModel,
    BartTokenizer,
)
from transformers.utils import logging


a__							:	Any  						=			['''bart.large''', '''bart.large.mnli''', '''bart.large.cnn''', '''bart_xsum/model.pt''']
a__							:	Union[str, Any]  						=			{'''bart.large''': BartModel, '''bart.large.mnli''': BartForSequenceClassification}
if version.parse(fairseq.__version__) < version.parse('''0.9.0'''):
      raise Exception('''requires fairseq >= 0.9.0''')


logging.set_verbosity_info()
a__							:	Tuple  						=			logging.get_logger(__name__)

a__							:	Union[str, Any]  						=			''' Hello world! cécé herlolip'''

a__							:	Any  						=			[
    ('''model.classification_heads.mnli.dense.weight''', '''classification_head.dense.weight'''),
    ('''model.classification_heads.mnli.dense.bias''', '''classification_head.dense.bias'''),
    ('''model.classification_heads.mnli.out_proj.weight''', '''classification_head.out_proj.weight'''),
    ('''model.classification_heads.mnli.out_proj.bias''', '''classification_head.out_proj.bias'''),
]







def 							UpperCAmelCase_(  a__					):



    """simple docstring"""



    SCREAMING_SNAKE_CASE							:							List[Any]								=      [
        """encoder.version""",
        """decoder.version""",
        """model.encoder.version""",
        """model.decoder.version""",
        """_float_tensor""",
    ]
    for k in ignore_keys:
        state_dict.pop(_lowercase      ,		_lowercase					)







def 							UpperCAmelCase_(  a__      ,		a__      ,		a__					):



    """simple docstring"""



    SCREAMING_SNAKE_CASE							:							Optional[int]								=      dct.pop(_lowercase					)
    SCREAMING_SNAKE_CASE							:							Optional[Any]								=      val







def 							UpperCAmelCase_(  a__					):



    """simple docstring"""



    SCREAMING_SNAKE_CASE							:							Tuple								=      torch.load(_lowercase      ,		map_location='''cpu'''					)
    SCREAMING_SNAKE_CASE							:							Union[str, Any]								=      torch.hub.load('''pytorch/fairseq'''      ,		'''bart.large.cnn'''					).eval()
    hub_interface.model.load_state_dict(sd['''model''']					)
    return hub_interface







def 							UpperCAmelCase_(  a__					):



    """simple docstring"""



    SCREAMING_SNAKE_CASE							:							Any								=      emb.weight.shape
    SCREAMING_SNAKE_CASE							:							Dict								=      nn.Linear(_lowercase      ,		_lowercase      ,		bias=_lowercase					)
    SCREAMING_SNAKE_CASE							:							int								=      emb.weight.data
    return lin_layer







@torch.no_grad()
def 							UpperCAmelCase_(  a__      ,		a__      ,		a__=None					):



    """simple docstring"""



    if not os.path.exists(_lowercase					):
        SCREAMING_SNAKE_CASE							:							str								=      torch.hub.load('''pytorch/fairseq'''      ,		_lowercase					).eval()
    else:
        SCREAMING_SNAKE_CASE							:							List[Any]								=      load_xsum_checkpoint(_lowercase					)

    bart.model.upgrade_state_dict(bart.model.state_dict()					)
    if hf_checkpoint_name is None:
        SCREAMING_SNAKE_CASE							:							Union[str, Any]								=      checkpoint_path.replace('''.'''      ,		'''-'''					)
    SCREAMING_SNAKE_CASE							:							List[Any]								=      BartConfig.from_pretrained(_lowercase					)
    SCREAMING_SNAKE_CASE							:							Tuple								=      bart.encode(_lowercase					).unsqueeze(0					)
    SCREAMING_SNAKE_CASE							:							Any								=      BartTokenizer.from_pretrained(_lowercase					).encode(_lowercase      ,		return_tensors='''pt'''					).unsqueeze(0					)
    if not torch.eq(_lowercase      ,		_lowercase					).all():
        raise ValueError(
            F"""converted tokenizer and pretrained tokenizer returned different output: {tokens} != {tokensa}"""					)

    if checkpoint_path == "bart.large.mnli":
        SCREAMING_SNAKE_CASE							:							int								=      bart.state_dict()
        remove_ignore_keys_(_lowercase					)
        SCREAMING_SNAKE_CASE							:							int								=      state_dict["""model.decoder.embed_tokens.weight"""]
        for src, dest in mnli_rename_keys:
            rename_key(_lowercase      ,		_lowercase      ,		_lowercase					)
        SCREAMING_SNAKE_CASE							:							Optional[int]								=      BartForSequenceClassification(_lowercase					).eval()
        model.load_state_dict(_lowercase					)
        SCREAMING_SNAKE_CASE							:							int								=      bart.predict('''mnli'''      ,		_lowercase      ,		return_logits=_lowercase					)
        SCREAMING_SNAKE_CASE							:							str								=      model(_lowercase					)[0]  # logits
    else:  # no classification heads to worry about
        SCREAMING_SNAKE_CASE							:							Dict								=      bart.model.state_dict()
        remove_ignore_keys_(_lowercase					)
        SCREAMING_SNAKE_CASE							:							int								=      state_dict["""decoder.embed_tokens.weight"""]
        SCREAMING_SNAKE_CASE							:							str								=      bart.extract_features(_lowercase					)
        if hf_checkpoint_name == "facebook/bart-large":
            SCREAMING_SNAKE_CASE							:							Optional[Any]								=      BartModel(_lowercase					).eval()
            model.load_state_dict(_lowercase					)
            SCREAMING_SNAKE_CASE							:							Optional[Any]								=      model(_lowercase					).model[0]
        else:
            SCREAMING_SNAKE_CASE							:							List[Any]								=      BartForConditionalGeneration(_lowercase					).eval()  # an existing summarization ckpt
            model.model.load_state_dict(_lowercase					)
            if hasattr(_lowercase      ,		'''lm_head'''					):
                SCREAMING_SNAKE_CASE							:							Tuple								=      make_linear_from_emb(model.model.shared					)
            SCREAMING_SNAKE_CASE							:							Optional[int]								=      model.model(_lowercase					)[0]

    # Check results
    if fairseq_output.shape != new_model_outputs.shape:
        raise ValueError(
            F"""`fairseq_output` shape and `new_model_output` shape are different: {fairseq_output.shape=}, {new_model_outputs.shape}"""					)
    if (fairseq_output != new_model_outputs).any().item():
        raise ValueError('''Some values in `fairseq_output` are different from `new_model_outputs`'''					)
    Path(_lowercase					).mkdir(exist_ok=_lowercase					)
    model.save_pretrained(_lowercase					)


if __name__ == "__main__":
      a__							:	Optional[Any]  						=			argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '''fairseq_path''', type=str, help='''bart.large, bart.large.cnn or a path to a model.pt on local filesystem.'''
      )
      parser.add_argument('''pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
      parser.add_argument(
          '''--hf_config''', default=None, type=str, help='''Which huggingface architecture to use: bart-large-xsum'''
      )
      a__							:	List[str]  						=			parser.parse_args()
      convert_bart_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, hf_checkpoint_name=args.hf_config)





"""simple docstring"""
import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionSAGPipeline,
    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 				lowerCamelCase  (			_lowerCAmelCase    ,						_lowerCAmelCase    ,						unittest.TestCase				):






					'''simple docstring'''





					_A		:			str						=     StableDiffusionSAGPipeline
					_A		:			Optional[Any]						=     TEXT_TO_IMAGE_PARAMS
					_A		:			Any						=     TEXT_TO_IMAGE_BATCH_PARAMS
					_A		:			Tuple						=     TEXT_TO_IMAGE_IMAGE_PARAMS
					_A		:			Tuple						=     TEXT_TO_IMAGE_IMAGE_PARAMS
					_A		:			List[str]						=     False






					def    lowerCAmelCase_				(       self:							Optional[Any]						)   ->	str:
											torch.manual_seed(0						)
											snake_case_ :Any						=			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_ :Any						=			DDIMScheduler(
											    beta_start=0.0_0_0_8_5						,     beta_end=0.0_1_2						,     beta_schedule="""scaled_linear"""						,     clip_sample=snake_case						,     set_alpha_to_one=snake_case						,     )
											torch.manual_seed(0						)
											snake_case_ :Optional[int]						=			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						,     )
											torch.manual_seed(0						)
											snake_case_ :Union[str, Any]						=			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_ :Tuple						=			CLIPTextModel(snake_case						)
											snake_case_ :str						=			CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""						)

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






					def    lowerCAmelCase_				(       self:							List[str]						,     snake_case:							Tuple						,     snake_case:							List[str]=0						)   ->	str:
											if str(snake_case						).startswith("""mps"""						):
																	snake_case_ :Tuple						=			torch.manual_seed(snake_case						)
											else:
																	snake_case_ :Optional[int]						=			torch.Generator(device=snake_case						).manual_seed(snake_case						)
											snake_case_ :Any						=			{
											    """prompt""": """.""",
											    """generator""": generator,
											    """num_inference_steps""": 2,
											    """guidance_scale""": 1.0,
											    """sag_scale""": 1.0,
											    """output_type""": """numpy""",
											}
											return inputs






					def    lowerCAmelCase_				(       self:							Optional[int]						)   ->	str:
											super().test_inference_batch_single_identical(expected_max_diff=3E-3						)

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






					'''simple docstring'''





					def    lowerCAmelCase_				(       self:							int						)   ->	str:
											# clean up the VRAM after each test
											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()






					def    lowerCAmelCase_				(       self:							int						)   ->	List[str]:
											snake_case_ :Any						=			StableDiffusionSAGPipeline.from_pretrained("""CompVis/stable-diffusion-v1-4"""						)
											snake_case_ :int						=			sag_pipe.to(snake_case						)
											sag_pipe.set_progress_bar_config(disable=snake_case						)

											snake_case_ :Union[str, Any]						=			"""."""
											snake_case_ :str						=			torch.manual_seed(0						)
											snake_case_ :str						=			sag_pipe(
											    [prompt]						,     generator=snake_case						,     guidance_scale=7.5						,     sag_scale=1.0						,     num_inference_steps=20						,     output_type="""np"""						)

											snake_case_ :List[Any]						=			output.images

											snake_case_ :Tuple						=			image[0, -3:, -3:, -1]

											assert image.shape == (1, 512, 512, 3)
											snake_case_ :List[Any]						=			np.array([0.1_5_6_8, 0.1_7_3_8, 0.1_6_9_5, 0.1_6_9_3, 0.1_5_0_7, 0.1_7_0_5, 0.1_5_4_7, 0.1_7_5_1, 0.1_9_4_9]						)

											assert np.abs(image_slice.flatten() - expected_slice						).max() < 5E-2






					def    lowerCAmelCase_				(       self:							Dict						)   ->	str:
											snake_case_ :Tuple						=			StableDiffusionSAGPipeline.from_pretrained("""stabilityai/stable-diffusion-2-1-base"""						)
											snake_case_ :Optional[int]						=			sag_pipe.to(snake_case						)
											sag_pipe.set_progress_bar_config(disable=snake_case						)

											snake_case_ :Tuple						=			"""."""
											snake_case_ :Union[str, Any]						=			torch.manual_seed(0						)
											snake_case_ :Tuple						=			sag_pipe(
											    [prompt]						,     generator=snake_case						,     guidance_scale=7.5						,     sag_scale=1.0						,     num_inference_steps=20						,     output_type="""np"""						)

											snake_case_ :Optional[int]						=			output.images

											snake_case_ :Optional[Any]						=			image[0, -3:, -3:, -1]

											assert image.shape == (1, 512, 512, 3)
											snake_case_ :Tuple						=			np.array([0.3_4_5_9, 0.2_8_7_6, 0.2_5_3_7, 0.3_0_0_2, 0.2_6_7_1, 0.2_1_6_0, 0.3_0_2_6, 0.2_2_6_2, 0.2_3_7_1]						)

											assert np.abs(image_slice.flatten() - expected_slice						).max() < 5E-2






					def    lowerCAmelCase_				(       self:							List[str]						)   ->	List[str]:
											snake_case_ :Optional[int]						=			StableDiffusionSAGPipeline.from_pretrained("""stabilityai/stable-diffusion-2-1-base"""						)
											snake_case_ :int						=			sag_pipe.to(snake_case						)
											sag_pipe.set_progress_bar_config(disable=snake_case						)

											snake_case_ :Tuple						=			"""."""
											snake_case_ :Optional[int]						=			torch.manual_seed(0						)
											snake_case_ :List[str]						=			sag_pipe(
											    [prompt]						,     width=768						,     height=512						,     generator=snake_case						,     guidance_scale=7.5						,     sag_scale=1.0						,     num_inference_steps=20						,     output_type="""np"""						,     )

											snake_case_ :Optional[Any]						=			output.images

											assert image.shape == (1, 512, 768, 3)
