






import unittest

import torch

from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, slow, torch_device

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin



class 							_A (    UpperCamelCase				,				UpperCamelCase				,				unittest.TestCase							):
    """simple docstring"""

    lowerCamelCase  :       str   =   UNetaDModel
    lowerCamelCase  :       List[Any]   =   'sample'



    @property
    def _a       (							self			:    int			)			->       int:
           __UpperCAmelCase    							=4
           __UpperCAmelCase    							=14
           __UpperCAmelCase    							=16

           __UpperCAmelCase    							=floats_tensor((batch_size, num_features, seq_len)			).to(__SCREAMING_SNAKE_CASE			)
           __UpperCAmelCase    							=torch.tensor([10] * batch_size			).to(__SCREAMING_SNAKE_CASE			)

           return {"sample": noise, "timestep": time_step}



    @property
    def _a       (							self			:    int			)			->       str:
           return (4, 14, 16)



    @property
    def _a       (							self			:    Union[str, Any]			)			->       int:
           return (4, 14, 16)



    def _a       (							self			:    Dict			)			->       Optional[Any]:
           pass



    def _a       (							self			:    Union[str, Any]			)			->       Optional[Any]:
           pass



    @unittest.skipIf(torch_device == """mps"""   ,    """mish op not supported in MPS"""			)
    def _a       (							self			:    Optional[Any]			)			->       int:
           super().test_determinism()



    @unittest.skipIf(torch_device == """mps"""   ,    """mish op not supported in MPS"""			)
    def _a       (							self			:    int			)			->       Optional[Any]:
           super().test_outputs_equivalence()



    @unittest.skipIf(torch_device == """mps"""   ,    """mish op not supported in MPS"""			)
    def _a       (							self			:    Optional[Any]			)			->       Optional[int]:
           super().test_from_save_pretrained()



    @unittest.skipIf(torch_device == """mps"""   ,    """mish op not supported in MPS"""			)
    def _a       (							self			:    str			)			->       Optional[int]:
           super().test_from_save_pretrained_variant()



    @unittest.skipIf(torch_device == """mps"""   ,    """mish op not supported in MPS"""			)
    def _a       (							self			:    Any			)			->       Dict:
           super().test_model_from_pretrained()



    @unittest.skipIf(torch_device == """mps"""   ,    """mish op not supported in MPS"""			)
    def _a       (							self			:    Any			)			->       List[str]:
           super().test_output()



    def _a       (							self			:    Optional[Any]			)			->       Optional[int]:
           __UpperCAmelCase    							={
               """block_out_channels""": (32, 64, 128, 256),
               """in_channels""": 14,
               """out_channels""": 14,
               """time_embedding_type""": """positional""",
               """use_timestep_embedding""": True,
               """flip_sin_to_cos""": False,
               """freq_shift""": 1.0,
               """out_block_type""": """OutConv1DBlock""",
               """mid_block_type""": """MidResTemporalBlock1D""",
               """down_block_types""": ("""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D"""),
               """up_block_types""": ("""UpResnetBlock1D""", """UpResnetBlock1D""", """UpResnetBlock1D"""),
               """act_fn""": """mish""",
           }
           __UpperCAmelCase    							=self.dummy_input
           return init_dict, inputs_dict



    @unittest.skipIf(torch_device == """mps"""   ,    """mish op not supported in MPS"""			)
    def _a       (							self			:    Tuple			)			->       Any:
           __UpperCAmelCase							,   __UpperCAmelCase    							=UNetaDModel.from_pretrained(
               """bglick13/hopper-medium-v2-value-function-hor32"""   ,    output_loading_info=__SCREAMING_SNAKE_CASE   ,    subfolder="""unet"""			)
           self.assertIsNotNone(__SCREAMING_SNAKE_CASE			)
           self.assertEqual(len(loading_info["""missing_keys"""]			)   ,    0			)

           model.to(__SCREAMING_SNAKE_CASE			)
           __UpperCAmelCase    							=model(**self.dummy_input			)

           assert image is not None, "Make sure output is not None"



    @unittest.skipIf(torch_device == """mps"""   ,    """mish op not supported in MPS"""			)
    def _a       (							self			:    str			)			->       Union[str, Any]:
           __UpperCAmelCase    							=UNetaDModel.from_pretrained("""bglick13/hopper-medium-v2-value-function-hor32"""   ,    subfolder="""unet"""			)
           torch.manual_seed(0			)
           if torch.cuda.is_available():
                  torch.cuda.manual_seed_all(0			)

           __UpperCAmelCase    							=model.config.in_channels
           __UpperCAmelCase    							=16
           __UpperCAmelCase    							=torch.randn((1, seq_len, num_features)			).permute(
               0   ,    2   ,    1			)  # match original, we can update values and remove
           __UpperCAmelCase    							=torch.full((num_features,)   ,    0			)

           with torch.no_grad():
                  __UpperCAmelCase    							=model(__SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE			).sample.permute(0   ,    2   ,    1			)

           __UpperCAmelCase    							=output[0, -3:, -3:].flatten()
           # fmt: off
           __UpperCAmelCase    							=torch.tensor([-2.137_172, 1.1_426_016, 0.3_688_687, -0.766_922, 0.7_303_146, 0.11_038_864, -0.4_760_633, 0.13_270_172, 0.02_591_348]			)
           # fmt: on
           self.assertTrue(torch.allclose(__SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE   ,    rtol=1e-3			)			)



    def _a       (							self			:    List[Any]			)			->       List[str]:
           # Not implemented yet for this UNet
           pass



    @slow
    def _a       (							self			:    str			)			->       int:
           __UpperCAmelCase    							="""harmonai/maestro-150k"""
           __UpperCAmelCase    							=UNetaDModel.from_pretrained(__SCREAMING_SNAKE_CASE   ,    subfolder="""unet"""			)
           model.to(__SCREAMING_SNAKE_CASE			)

           __UpperCAmelCase    							=65536
           __UpperCAmelCase    							=torch.sin(torch.arange(__SCREAMING_SNAKE_CASE			)[None, None, :].repeat(1   ,    2   ,    1			)			).to(__SCREAMING_SNAKE_CASE			)
           __UpperCAmelCase    							=torch.tensor([1]			).to(__SCREAMING_SNAKE_CASE			)

           with torch.no_grad():
                  __UpperCAmelCase    							=model(__SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE			).sample

           __UpperCAmelCase    							=output.abs().sum()
           __UpperCAmelCase    							=output.abs().max()

           assert (output_sum - 224.0_896).abs() < 0.5
           assert (output_max - 0.0_607).abs() < 4e-4




class 							_A (    UpperCamelCase				,				UpperCamelCase				,				unittest.TestCase							):
    """simple docstring"""

    lowerCamelCase  :       Optional[Any]   =   UNetaDModel
    lowerCamelCase  :       str   =   'sample'



    @property
    def _a       (							self			:    Optional[Any]			)			->       Optional[Any]:
           __UpperCAmelCase    							=4
           __UpperCAmelCase    							=14
           __UpperCAmelCase    							=16

           __UpperCAmelCase    							=floats_tensor((batch_size, num_features, seq_len)			).to(__SCREAMING_SNAKE_CASE			)
           __UpperCAmelCase    							=torch.tensor([10] * batch_size			).to(__SCREAMING_SNAKE_CASE			)

           return {"sample": noise, "timestep": time_step}



    @property
    def _a       (							self			:    Tuple			)			->       List[Any]:
           return (4, 14, 16)



    @property
    def _a       (							self			:    Optional[Any]			)			->       Union[str, Any]:
           return (4, 14, 1)



    @unittest.skipIf(torch_device == """mps"""   ,    """mish op not supported in MPS"""			)
    def _a       (							self			:    List[str]			)			->       Optional[int]:
           super().test_determinism()



    @unittest.skipIf(torch_device == """mps"""   ,    """mish op not supported in MPS"""			)
    def _a       (							self			:    Optional[int]			)			->       Union[str, Any]:
           super().test_outputs_equivalence()



    @unittest.skipIf(torch_device == """mps"""   ,    """mish op not supported in MPS"""			)
    def _a       (							self			:    Optional[Any]			)			->       Dict:
           super().test_from_save_pretrained()



    @unittest.skipIf(torch_device == """mps"""   ,    """mish op not supported in MPS"""			)
    def _a       (							self			:    List[str]			)			->       str:
           super().test_from_save_pretrained_variant()



    @unittest.skipIf(torch_device == """mps"""   ,    """mish op not supported in MPS"""			)
    def _a       (							self			:    Optional[int]			)			->       Any:
           super().test_model_from_pretrained()



    @unittest.skipIf(torch_device == """mps"""   ,    """mish op not supported in MPS"""			)
    def _a       (							self			:    int			)			->       Optional[Any]:
           # UNetRL is a value-function is different output shape
           __UpperCAmelCase							,   __UpperCAmelCase    							=self.prepare_init_args_and_inputs_for_common()
           __UpperCAmelCase    							=self.model_class(**__SCREAMING_SNAKE_CASE			)
           model.to(__SCREAMING_SNAKE_CASE			)
           model.eval()

           with torch.no_grad():
                  __UpperCAmelCase    							=model(**__SCREAMING_SNAKE_CASE			)

                  if isinstance(__SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE			):
                         __UpperCAmelCase    							=output.sample

           self.assertIsNotNone(__SCREAMING_SNAKE_CASE			)
           __UpperCAmelCase    							=torch.Size((inputs_dict["""sample"""].shape[0], 1)			)
           self.assertEqual(output.shape   ,    __SCREAMING_SNAKE_CASE   ,    """Input and output shapes do not match"""			)



    def _a       (							self			:    Tuple			)			->       Any:
           pass



    def _a       (							self			:    str			)			->       Union[str, Any]:
           pass



    def _a       (							self			:    Dict			)			->       str:
           __UpperCAmelCase    							={
               """in_channels""": 14,
               """out_channels""": 14,
               """down_block_types""": ["""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D"""],
               """up_block_types""": [],
               """out_block_type""": """ValueFunction""",
               """mid_block_type""": """ValueFunctionMidBlock1D""",
               """block_out_channels""": [32, 64, 128, 256],
               """layers_per_block""": 1,
               """downsample_each_block""": True,
               """use_timestep_embedding""": True,
               """freq_shift""": 1.0,
               """flip_sin_to_cos""": False,
               """time_embedding_type""": """positional""",
               """act_fn""": """mish""",
           }
           __UpperCAmelCase    							=self.dummy_input
           return init_dict, inputs_dict



    @unittest.skipIf(torch_device == """mps"""   ,    """mish op not supported in MPS"""			)
    def _a       (							self			:    Tuple			)			->       List[Any]:
           __UpperCAmelCase							,   __UpperCAmelCase    							=UNetaDModel.from_pretrained(
               """bglick13/hopper-medium-v2-value-function-hor32"""   ,    output_loading_info=__SCREAMING_SNAKE_CASE   ,    subfolder="""value_function"""			)
           self.assertIsNotNone(__SCREAMING_SNAKE_CASE			)
           self.assertEqual(len(vf_loading_info["""missing_keys"""]			)   ,    0			)

           value_function.to(__SCREAMING_SNAKE_CASE			)
           __UpperCAmelCase    							=value_function(**self.dummy_input			)

           assert image is not None, "Make sure output is not None"



    @unittest.skipIf(torch_device == """mps"""   ,    """mish op not supported in MPS"""			)
    def _a       (							self			:    List[Any]			)			->       int:
           __UpperCAmelCase							,   __UpperCAmelCase    							=UNetaDModel.from_pretrained(
               """bglick13/hopper-medium-v2-value-function-hor32"""   ,    output_loading_info=__SCREAMING_SNAKE_CASE   ,    subfolder="""value_function"""			)
           torch.manual_seed(0			)
           if torch.cuda.is_available():
                  torch.cuda.manual_seed_all(0			)

           __UpperCAmelCase    							=value_function.config.in_channels
           __UpperCAmelCase    							=14
           __UpperCAmelCase    							=torch.randn((1, seq_len, num_features)			).permute(
               0   ,    2   ,    1			)  # match original, we can update values and remove
           __UpperCAmelCase    							=torch.full((num_features,)   ,    0			)

           with torch.no_grad():
                  __UpperCAmelCase    							=value_function(__SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE			).sample

           # fmt: off
           __UpperCAmelCase    							=torch.tensor([165.25] * seq_len			)
           # fmt: on
           self.assertTrue(torch.allclose(__SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE   ,    rtol=1e-3			)			)



    def _a       (							self			:    Dict			)			->       Optional[int]:
           # Not implemented yet for this UNet
           pass












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()
__A  							=  logging.get_logger(__name__)

__A  							=  "Hello world! cécé herlolip"







def 			lowercase__  (				A_:	str     ,			A_:	str     ,			A_:	bool		)  ->							str:







       """simple docstring"""






       __UpperCAmelCase							,   __UpperCAmelCase    							=os.path.split(A_		)
       __UpperCAmelCase    							=DataaVecTextModel.from_pretrained(
           A_     ,			checkpoint_file=A_		)
       dataavec.eval()  # disable dropout
       __UpperCAmelCase    							=dataavec.models[0]
       __UpperCAmelCase    							=dataavec_model.encoder.sentence_encoder
       __UpperCAmelCase    							=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:
              __UpperCAmelCase    							=dataavec.model.classification_heads["""mnli"""].out_proj.weight.shape[0]
       print("""Our BERT config:"""     ,			A_		)

       __UpperCAmelCase    							=DataaVecTextForSequenceClassification(A_		) if classification_head else DataaVecTextForMaskedLM(A_		)
       model.eval()

       # Now let's copy all the weights.
       # Embeddings
       __UpperCAmelCase    							=dataavec_sent_encoder.embed_tokens.weight
       __UpperCAmelCase    							=dataavec_sent_encoder.embed_positions.weight
       __UpperCAmelCase    							=torch.zeros_like(
           model.dataavec_text.embeddings.token_type_embeddings.weight		)  # just zero them out b/c data2vec doesn't use them.
       __UpperCAmelCase    							=dataavec_sent_encoder.layernorm_embedding.weight
       __UpperCAmelCase    							=dataavec_sent_encoder.layernorm_embedding.bias

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

              # self attention
              __UpperCAmelCase    							=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)		)}'''
              )

              __UpperCAmelCase    							=dataavec_layer.self_attn.q_proj.weight
              __UpperCAmelCase    							=dataavec_layer.self_attn.q_proj.bias
              __UpperCAmelCase    							=dataavec_layer.self_attn.k_proj.weight
              __UpperCAmelCase    							=dataavec_layer.self_attn.k_proj.bias
              __UpperCAmelCase    							=dataavec_layer.self_attn.v_proj.weight
              __UpperCAmelCase    							=dataavec_layer.self_attn.v_proj.bias

              # self-attention output
              __UpperCAmelCase    							=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}'''
              __UpperCAmelCase    							=dataavec_layer.self_attn.out_proj.weight
              __UpperCAmelCase    							=dataavec_layer.self_attn.out_proj.bias
              __UpperCAmelCase    							=dataavec_layer.self_attn_layer_norm.weight
              __UpperCAmelCase    							=dataavec_layer.self_attn_layer_norm.bias

              # intermediate
              __UpperCAmelCase    							=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}'''
              __UpperCAmelCase    							=dataavec_layer.fca.weight
              __UpperCAmelCase    							=dataavec_layer.fca.bias

              # output
              __UpperCAmelCase    							=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}'''
              __UpperCAmelCase    							=dataavec_layer.fca.weight
              __UpperCAmelCase    							=dataavec_layer.fca.bias
              __UpperCAmelCase    							=dataavec_layer.final_layer_norm.weight
              __UpperCAmelCase    							=dataavec_layer.final_layer_norm.bias
              # end of layer

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

       # Let's check that we get the same results.
       __UpperCAmelCase    							=dataavec.encode(A_		).unsqueeze(0		)  # batch of size 1

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

       pathlib.Path(A_		).mkdir(parents=A_     ,			exist_ok=A_		)
       print(F'''Saving model to {pytorch_dump_folder_path}'''		)
       model.save_pretrained(A_		)


if __name__ == "__main__":
   __A  							=  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."
   )
   __A  							=  parser.parse_args()
   convert_dataavec_checkpoint_to_pytorch(
       args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
   )




