



'''simple docstring'''





import copy
import tempfile
import unittest

from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch

       from transformers import MaMaaaForConditionalGeneration, MaMaaaModel, MaMaaaTokenizer
       from transformers.models.mam_aaa.modeling_mam_aaa import MaMaaaDecoder, MaMaaaEncoder





def 		__snake_case(							_lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase=None	,  _lowerCAmelCase=None	,  _lowerCAmelCase=None	,  _lowerCAmelCase=None	,  _lowerCAmelCase=None	,  )						->					int:
      if attention_mask is None:
            snake_case__    :       int       		= input_ids.ne(config.pad_token_id						)
      if decoder_attention_mask is None:
            snake_case__    :       Dict       		= decoder_input_ids.ne(config.pad_token_id						)
      if head_mask is None:
            snake_case__    :       int       		= torch.ones(config.encoder_layers	,  config.encoder_attention_heads	,  device=_lowerCAmelCase						)
      if decoder_head_mask is None:
            snake_case__    :       Any       		= torch.ones(config.decoder_layers	,  config.decoder_attention_heads	,  device=_lowerCAmelCase						)
      if cross_attn_head_mask is None:
            snake_case__    :       Dict       		= torch.ones(config.decoder_layers	,  config.decoder_attention_heads	,  device=_lowerCAmelCase						)
      return {
          "input_ids": input_ids,
          "decoder_input_ids": decoder_input_ids,
          "attention_mask": attention_mask,
          "decoder_attention_mask": attention_mask,
          "head_mask": head_mask,
          "decoder_head_mask": decoder_head_mask,
          "cross_attn_head_mask": cross_attn_head_mask,
      }







class        UpperCAmelCase_			:
    """simple docstring"""







    def __init__(       self   :	Union[str, Any]					,	snake_case_   :	Optional[Any]					,	snake_case_   :	Any=13					,	snake_case_   :	List[Any]=7					,	snake_case_   :	str=True					,	snake_case_   :	Optional[Any]=False					,	snake_case_   :	Optional[Any]=99					,	snake_case_   :	str=16					,	snake_case_   :	Optional[Any]=2					,	snake_case_   :	str=4					,	snake_case_   :	List[Any]=4					,	snake_case_   :	Any="relu"					,	snake_case_   :	Any=0.1					,	snake_case_   :	List[Any]=0.1					,	snake_case_   :	Any=0.0					,	snake_case_   :	Optional[Any]=0.0					,	snake_case_   :	Any=20					,	snake_case_   :	int=2					,	snake_case_   :	Optional[int]=1					,	snake_case_   :	List[Any]=0					,	):
          snake_case__    :       List[Any]       		= parent
          snake_case__    :       Tuple       		= batch_size
          snake_case__    :       Any       		= seq_length
          snake_case__    :       Tuple       		= is_training
          snake_case__    :       int       		= use_labels
          snake_case__    :       str       		= vocab_size
          snake_case__    :       Dict       		= hidden_size
          snake_case__    :       int       		= num_hidden_layers
          snake_case__    :       List[Any]       		= num_attention_heads
          snake_case__    :       Optional[int]       		= intermediate_size
          snake_case__    :       Optional[Any]       		= hidden_act
          snake_case__    :       Optional[int]       		= hidden_dropout_prob
          snake_case__    :       Optional[int]       		= attention_probs_dropout_prob
          snake_case__    :       str       		= encoder_layerdrop
          snake_case__    :       Union[str, Any]       		= decoder_layerdrop
          snake_case__    :       Union[str, Any]       		= max_position_embeddings
          snake_case__    :       List[Any]       		= eos_token_id
          snake_case__    :       Optional[int]       		= pad_token_id
          snake_case__    :       Any       		= bos_token_id




    def lowerCamelCase			(       self   :	Optional[Any]		):
          snake_case__    :       Dict       		= ids_tensor([self.batch_size, self.seq_length]					,	self.vocab_size		)
          snake_case__    :       str       		= self.eos_token_id  # Eos Token
          snake_case__    :       Tuple       		= ids_tensor([self.batch_size, self.seq_length]					,	self.vocab_size		)

          # we need to clamp the input ids here to avoid having pad token in between
          # this is because for M2M100 the position_ids are prepared such that
          # all pad tokens have pos id = 2 and rest are between 2..seq_length
          # and the seq_length here is seq_length - num_pad_tokens
          # but when using past, there is no way of knowing if the past input ids had
          # pad tokens in them, which results in incorrect seq_lenth and which in turn results in
          # position_ids being off by num_pad_tokens in past input
          snake_case__    :       str       		= input_ids.clamp(self.pad_token_id + 1		)
          snake_case__    :       Any       		= decoder_input_ids.clamp(self.pad_token_id + 1		)

          snake_case__    :       Union[str, Any]       		= self.get_config()
          snake_case__    :       List[str]       		= prepare_mam_aaa_inputs_dict(snake_case_					,	snake_case_					,	snake_case_		)
          return config, inputs_dict




    def lowerCamelCase			(       self   :	List[str]		):
          return MaMaaaConfig(
              vocab_size=self.vocab_size					,	d_model=self.hidden_size					,	encoder_layers=self.num_hidden_layers					,	decoder_layers=self.num_hidden_layers					,	encoder_attention_heads=self.num_attention_heads					,	decoder_attention_heads=self.num_attention_heads					,	encoder_ffn_dim=self.intermediate_size					,	decoder_ffn_dim=self.intermediate_size					,	dropout=self.hidden_dropout_prob					,	attention_dropout=self.attention_probs_dropout_prob					,	encoder_layerdrop=self.encoder_layerdrop					,	decoder_layerdrop=self.decoder_layerdrop					,	max_position_embeddings=self.max_position_embeddings					,	eos_token_id=self.eos_token_id					,	bos_token_id=self.bos_token_id					,	pad_token_id=self.pad_token_id					,	)




    def lowerCamelCase			(       self   :	List[Any]		):
          snake_case__		,							snake_case__    :       int       		= self.prepare_config_and_inputs()
          return config, inputs_dict




    def lowerCamelCase			(       self   :	List[Any]					,	snake_case_   :	Any					,	snake_case_   :	Optional[Any]		):
          snake_case__    :       int       		= MaMaaaModel(config=snake_case_		).get_decoder().to(snake_case_		).eval()
          snake_case__    :       List[Any]       		= inputs_dict["""input_ids"""]
          snake_case__    :       str       		= inputs_dict["""attention_mask"""]
          snake_case__    :       Any       		= inputs_dict["""head_mask"""]

          # first forward pass
          snake_case__    :       Any       		= model(snake_case_					,	attention_mask=snake_case_					,	head_mask=snake_case_					,	use_cache=snake_case_		)

          snake_case__		,							snake_case__    :       Dict       		= outputs.to_tuple()

          # create hypothetical multiple next token and extent to next_input_ids
          snake_case__    :       int       		= ids_tensor((self.batch_size, 3)					,	config.vocab_size		)
          snake_case__    :       Optional[Any]       		= ids_tensor((self.batch_size, 3)					,	2		)

          # append to next input_ids and
          snake_case__    :       Tuple       		= torch.cat([input_ids, next_tokens]					,	dim=-1		)
          snake_case__    :       Dict       		= torch.cat([attention_mask, next_attn_mask]					,	dim=-1		)

          snake_case__    :       List[Any]       		= model(snake_case_					,	attention_mask=snake_case_		)["""last_hidden_state"""]
          snake_case__    :       str       		= model(snake_case_					,	attention_mask=snake_case_					,	past_key_values=snake_case_		)[
              """last_hidden_state"""
          ]

          # select random slice
          snake_case__    :       Dict       		= ids_tensor((1,)					,	output_from_past.shape[-1]		).item()
          snake_case__    :       Optional[Any]       		= output_from_no_past[:, -3:, random_slice_idx].detach()
          snake_case__    :       Optional[int]       		= output_from_past[:, :, random_slice_idx].detach()

          self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]		)

          # test that outputs are equal for slice
          self.parent.assertTrue(torch.allclose(snake_case_					,	snake_case_					,	atol=1E-2		)		)




    def lowerCamelCase			(       self   :	Dict					,	snake_case_   :	Tuple					,	snake_case_   :	Tuple		):
          snake_case__    :       Any       		= MaMaaaModel(config=snake_case_		).to(snake_case_		).eval()
          snake_case__    :       Dict       		= model(**snake_case_		)

          snake_case__    :       Optional[int]       		= outputs.encoder_last_hidden_state
          snake_case__    :       Optional[int]       		= outputs.last_hidden_state

          with tempfile.TemporaryDirectory() as tmpdirname:
                snake_case__    :       int       		= model.get_encoder()
                encoder.save_pretrained(snake_case_		)
                snake_case__    :       str       		= MaMaaaEncoder.from_pretrained(snake_case_		).to(snake_case_		)

          snake_case__    :       Optional[Any]       		= encoder(inputs_dict["""input_ids"""]					,	attention_mask=inputs_dict["""attention_mask"""]		)[
              0
          ]

          self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1E-3		)

          with tempfile.TemporaryDirectory() as tmpdirname:
                snake_case__    :       Union[str, Any]       		= model.get_decoder()
                decoder.save_pretrained(snake_case_		)
                snake_case__    :       str       		= MaMaaaDecoder.from_pretrained(snake_case_		).to(snake_case_		)

          snake_case__    :       Optional[Any]       		= decoder(
              input_ids=inputs_dict["""decoder_input_ids"""]					,	attention_mask=inputs_dict["""decoder_attention_mask"""]					,	encoder_hidden_states=snake_case_					,	encoder_attention_mask=inputs_dict["""attention_mask"""]					,	)[0]

          self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1E-3		)







@require_torch
class        UpperCAmelCase_			(			_a						,						_a						,						_a						,						unittest.TestCase      ):
    """simple docstring"""







    lowercase   =						(
        (
            MaMaaaModel,
            MaMaaaForConditionalGeneration,
        )
        if is_torch_available()
        else ()
    )
    lowercase   =						(MaMaaaForConditionalGeneration,) if is_torch_available() else ()
    lowercase   =						(
        {
            "conversational": MaMaaaForConditionalGeneration,
            "feature-extraction": MaMaaaModel,
            "summarization": MaMaaaForConditionalGeneration,
            "text2text-generation": MaMaaaForConditionalGeneration,
            "translation": MaMaaaForConditionalGeneration,
        }
        if is_torch_available()
        else {}
    )
    lowercase   =						True
    lowercase   =						True
    lowercase   =						False
    lowercase   =						False




    def lowerCamelCase			(       self   :	Any					,	snake_case_   :	List[Any]					,	snake_case_   :	List[str]					,	snake_case_   :	Any					,	snake_case_   :	Optional[Any]					,	snake_case_   :	List[Any]		):
          if pipeline_test_casse_name == "TranslationPipelineTests":
                # Get `ValueError: Translation requires a `src_lang` and a `tgt_lang` for this model`.
                # `M2M100Config` was never used in pipeline tests: cannot create a simple tokenizer.
                return True

          return False




    def lowerCamelCase			(       self   :	Tuple		):
          snake_case__    :       str       		= MaMaaaModelTester(self		)
          snake_case__    :       Optional[Any]       		= ConfigTester(self					,	config_class=snake_case_		)




    def lowerCamelCase			(       self   :	Optional[int]		):
          self.config_tester.run_common_tests()




    def lowerCamelCase			(       self   :	int		):
          snake_case__		,							snake_case__    :       List[str]       		= self.model_tester.prepare_config_and_inputs()
          for model_class in self.all_model_classes:
                snake_case__    :       List[str]       		= model_class(snake_case_		)

                with tempfile.TemporaryDirectory() as tmpdirname:
                      model.save_pretrained(snake_case_		)
                      snake_case__		,							snake_case__    :       List[Any]       		= model_class.from_pretrained(snake_case_					,	output_loading_info=snake_case_		)
                self.assertEqual(info["""missing_keys"""]					,	[]		)




    def lowerCamelCase			(       self   :	List[str]		):
          snake_case__    :       Optional[int]       		= self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_decoder_model_past_large_inputs(*snake_case_		)




    def lowerCamelCase			(       self   :	List[Any]		):
          snake_case__    :       List[str]       		= self.model_tester.prepare_config_and_inputs_for_common()
          self.model_tester.check_encoder_decoder_model_standalone(*snake_case_		)




    def lowerCamelCase			(       self   :	Optional[int]		):
          snake_case__		,							snake_case__    :       Optional[int]       		= self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in (MaMaaaModel, MaMaaaForConditionalGeneration):
                snake_case__    :       Tuple       		= model_class(snake_case_		)
                model.to(snake_case_		)
                model.eval()

                snake_case__    :       Any       		= copy.deepcopy(self._prepare_for_class(snake_case_					,	snake_case_		)		)

                if not self.is_encoder_decoder:
                      snake_case__    :       int       		= inputs["""input_ids"""]
                      del inputs["input_ids"]
                else:
                      snake_case__    :       Optional[int]       		= inputs["""input_ids"""]
                      snake_case__    :       Any       		= inputs.get("""decoder_input_ids"""					,	snake_case_		)
                      del inputs["input_ids"]
                      inputs.pop("""decoder_input_ids"""					,	snake_case_		)

                snake_case__    :       List[str]       		= model.get_input_embeddings()
                if not self.is_encoder_decoder:
                      snake_case__    :       int       		= wte(snake_case_		)
                else:
                      snake_case__    :       int       		= wte(snake_case_		)
                      snake_case__    :       int       		= wte(snake_case_		)

                with torch.no_grad():
                      model(**snake_case_		)[0]




    def lowerCamelCase			(       self   :	Optional[int]		):
          snake_case__		,							snake_case__    :       Optional[Any]       		= self.model_tester.prepare_config_and_inputs()
          snake_case__    :       Tuple       		= input_dict["""input_ids"""]
          snake_case__    :       Tuple       		= input_ids.ne(1		).to(snake_case_		)
          snake_case__    :       Optional[Any]       		= MaMaaaForConditionalGeneration(snake_case_		).eval().to(snake_case_		)
          if torch_device == "cuda":
                model.half()
          model.generate(snake_case_					,	attention_mask=snake_case_		)
          model.generate(num_beams=4					,	do_sample=snake_case_					,	early_stopping=snake_case_					,	num_return_sequences=3		)





def 		__snake_case(							_lowerCAmelCase						)						->					Optional[Any]:
      return torch.tensor(_lowerCAmelCase	,  dtype=torch.long	,  device=_lowerCAmelCase						)


__a  			=						1E-4







@require_torch
@require_sentencepiece
@require_tokenizers
@slow
class        UpperCAmelCase_			(			unittest.TestCase      ):
    """simple docstring"""







    @cached_property
    def lowerCamelCase			(       self   :	int		):
          return MaMaaaTokenizer.from_pretrained("""facebook/m2m100_418M"""		)




    def lowerCamelCase			(       self   :	Dict		):
          snake_case__    :       str       		= MaMaaaModel.from_pretrained("""facebook/m2m100_418M"""		).to(snake_case_		)
          snake_case__    :       Dict       		= _long_tensor([[128_028, 98, 12, 30_527, 2_732, 159, 7_755, 61_904, 39_144, 38, 2]]		)
          snake_case__    :       Any       		= _long_tensor([[2, 128_028, 98, 12, 30_527, 2_732, 159, 7_755, 61_904, 39_144, 38]]		)
          snake_case__    :       str       		= prepare_mam_aaa_inputs_dict(model.config					,	snake_case_					,	snake_case_		)
          with torch.no_grad():
                snake_case__    :       int       		= model(**snake_case_		)[0]
          snake_case__    :       Optional[Any]       		= torch.Size((1, 11, 1_024)		)
          self.assertEqual(output.shape					,	snake_case_		)
          # change to expected output here
          snake_case__    :       Tuple       		= torch.tensor(
              [[-0.7780, -0.1676, 0.1038], [-6.7556, -1.3992, 0.0567], [-7.5383, -0.5920, -0.2779]]					,	device=snake_case_		)
          self.assertTrue(torch.allclose(output[:, :3, :3]					,	snake_case_					,	atol=snake_case_		)		)




    def lowerCamelCase			(       self   :	Optional[Any]		):
          snake_case__    :       Dict       		= MaMaaaForConditionalGeneration.from_pretrained("""facebook/m2m100_418M"""		).to(snake_case_		)

          # change to intended input
          snake_case__    :       Dict       		= _long_tensor([[128_028, 98, 12, 30_527, 2_732, 159, 7_755, 61_904, 39_144, 38, 2]]		)
          snake_case__    :       str       		= _long_tensor([[2, 128_028, 98, 12, 30_527, 2_732, 159, 7_755, 61_904, 39_144, 38]]		)
          snake_case__    :       Dict       		= prepare_mam_aaa_inputs_dict(model.config					,	snake_case_					,	snake_case_		)
          with torch.no_grad():
                snake_case__    :       int       		= model(**snake_case_		)[0]
          snake_case__    :       Tuple       		= torch.Size((1, 11, model.config.vocab_size)		)
          self.assertEqual(output.shape					,	snake_case_		)
          # change to expected output here
          snake_case__    :       List[str]       		= torch.tensor(
              [[-1.0448, -1.0411, 3.7992], [-3.2191, -3.2386, -1.3451], [-3.6210, -3.5993, 0.4925]]					,	device=snake_case_		)
          self.assertTrue(torch.allclose(output[:, :3, :3]					,	snake_case_					,	atol=snake_case_		)		)




    def lowerCamelCase			(       self   :	Tuple		):
          snake_case__    :       Any       		= MaMaaaForConditionalGeneration.from_pretrained("""facebook/m2m100_418M"""		).to(snake_case_		)
          snake_case__    :       Any       		= MaMaaaTokenizer.from_pretrained("""facebook/m2m100_418M"""					,	src_lang="""fr"""					,	tgt_lang="""en"""		)

          snake_case__    :       List[str]       		= [
              """L'affaire NSA souligne l'absence totale de débat sur le renseignement""",
              """Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.""",
              """Lorsque François Hollande téléphone à Barack Obama ou quand le ministre des affaires étrangères Laurent"""
              """ Fabius convoque l'ambassadeur des Etats-Unis, ils réagissent à une vraie découverte, qui est celle de"""
              """ l'ampleur de la surveillance américaine sur l'ensemble des communications en France.""",
          ]

          # The below article tests that we don't add any hypotheses outside of the top n_beams
          snake_case__    :       Union[str, Any]       		= tokenizer(snake_case_					,	padding=snake_case_					,	return_tensors="""pt"""		)

          snake_case__    :       Any       		= model.generate(
              input_ids=dct["""input_ids"""].to(snake_case_		)					,	attention_mask=dct["""attention_mask"""].to(snake_case_		)					,	num_beams=5					,	forced_bos_token_id=tokenizer.get_lang_id("""en"""		)					,	)

          snake_case__    :       Tuple       		= [
              """The NSA case highlights the total absence of intelligence debate""",
              """I think there are two levels of response from the French government.""",
              """When François Hollande calls Barack Obama or when Foreign Minister Laurent Fabius calls the U.S."""
              """ Ambassador, they respond to a real discovery, which is that of the scale of U.S. surveillance on all"""
              """ communications in France.""",
          ]

          snake_case__    :       str       		= tokenizer.batch_decode(
              hypotheses_batch.tolist()					,	clean_up_tokenization_spaces=snake_case_					,	skip_special_tokens=snake_case_		)
          assert generated == expected_en





'''simple docstring'''





import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel





def 		__snake_case(							_lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase						)						->					int:
      snake_case__    :       Tuple       		= OmegaConf.load(_lowerCAmelCase						)
      snake_case__    :       Tuple       		= torch.load(_lowerCAmelCase	,  map_location="""cpu"""						)["""model"""]
      snake_case__    :       int       		= list(state_dict.keys()						)

      # extract state_dict for VQVAE
      snake_case__    :       Dict       		= {}
      snake_case__    :       Tuple       		= """first_stage_model."""
      for key in keys:
            if key.startswith(_lowerCAmelCase						):
                  snake_case__    :       Tuple       		= state_dict[key]

    # extract state_dict for UNetLDM
      snake_case__    :       Optional[Any]       		= {}
      snake_case__    :       Optional[int]       		= """model.diffusion_model."""
      for key in keys:
            if key.startswith(_lowerCAmelCase						):
                  snake_case__    :       Tuple       		= state_dict[key]

      snake_case__    :       Dict       		= config.model.params.first_stage_config.params
      snake_case__    :       Optional[Any]       		= config.model.params.unet_config.params

      snake_case__    :       Tuple       		= VQModel(**_lowerCAmelCase						).eval()
      vqvae.load_state_dict(_lowerCAmelCase						)

      snake_case__    :       List[Any]       		= UNetLDMModel(**_lowerCAmelCase						).eval()
      unet.load_state_dict(_lowerCAmelCase						)

      snake_case__    :       List[Any]       		= DDIMScheduler(
          timesteps=config.model.params.timesteps	,  beta_schedule="""scaled_linear"""	,  beta_start=config.model.params.linear_start	,  beta_end=config.model.params.linear_end	,  clip_sample=_lowerCAmelCase	,  )

      snake_case__    :       List[str]       		= LDMPipeline(_lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase						)
      pipeline.save_pretrained(_lowerCAmelCase						)


if __name__ == "__main__":
       __a  			=						argparse.ArgumentParser()
       parser.add_argument("--checkpoint_path", type=str, required=True)
       parser.add_argument("--config_path", type=str, required=True)
       parser.add_argument("--output_path", type=str, required=True)
       __a  			=						parser.parse_args()

       convert_ldm_original(args.checkpoint_path, args.config_path, args.output_path)
