

from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


lowerCamelCase_    =					logging.get_logger(__name__)

lowerCamelCase_    =					{
    '''facebook/mbart-large-cc25''': '''https://huggingface.co/facebook/mbart-large-cc25/resolve/main/config.json''',
    # See all MBART models at https://huggingface.co/models?filter=mbart
}







class  __lowerCamelCase				(	__snake_case	):
      lowerCamelCase_	:      Union[str, Any]          =					'mbart'
      lowerCamelCase_	:      int          =					['past_key_values']
      lowerCamelCase_	:      List[Any]          =					{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}







      def __init__(     self							,  lowerCamelCase=50265							,  lowerCamelCase=1024							,  lowerCamelCase=12							,  lowerCamelCase=4096							,  lowerCamelCase=16							,  lowerCamelCase=12							,  lowerCamelCase=4096							,  lowerCamelCase=16							,  lowerCamelCase=0.0							,  lowerCamelCase=0.0							,  lowerCamelCase=True							,  lowerCamelCase=True							,  lowerCamelCase="gelu"							,  lowerCamelCase=1024							,  lowerCamelCase=0.1							,  lowerCamelCase=0.0							,  lowerCamelCase=0.0							,  lowerCamelCase=0.02							,  lowerCamelCase=0.0							,  lowerCamelCase=False							,  lowerCamelCase=1							,  lowerCamelCase=0							,  lowerCamelCase=2							,  lowerCamelCase=2							,  **lowerCamelCase							,  )						->       Tuple:
        snake_case_       						=      vocab_size
        snake_case_       						=      max_position_embeddings
        snake_case_       						=      d_model
        snake_case_       						=      encoder_ffn_dim
        snake_case_       						=      encoder_layers
        snake_case_       						=      encoder_attention_heads
        snake_case_       						=      decoder_ffn_dim
        snake_case_       						=      decoder_layers
        snake_case_       						=      decoder_attention_heads
        snake_case_       						=      dropout
        snake_case_       						=      attention_dropout
        snake_case_       						=      activation_dropout
        snake_case_       						=      activation_function
        snake_case_       						=      init_std
        snake_case_       						=      encoder_layerdrop
        snake_case_       						=      decoder_layerdrop
        snake_case_       						=      classifier_dropout
        snake_case_       						=      use_cache
        snake_case_       						=      encoder_layers
        snake_case_       						=      scale_embedding  # scale factor will be sqrt(d_model) if True
        super().__init__(
            pad_token_id=lowerCamelCase							,  bos_token_id=lowerCamelCase							,  eos_token_id=lowerCamelCase							,  is_encoder_decoder=lowerCamelCase							,  forced_eos_token_id=lowerCamelCase							,  **lowerCamelCase							,  )







class  __lowerCamelCase				(	__snake_case	):







      @property
      def 	lowerCAmelCase_    (     self   )						->       Mapping[str, Mapping[int, str]]:
        if self.task in ["default", "seq2seq-lm"]:
          snake_case_       						=      OrderedDict(
              [
                  ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                  ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
              ]   )

          if self.use_past:
            snake_case_       						=      {0: """batch"""}
            snake_case_       						=      {0: """batch""", 1: """past_decoder_sequence + sequence"""}
          else:
            snake_case_       						=      {0: """batch""", 1: """decoder_sequence"""}
            snake_case_       						=      {0: """batch""", 1: """decoder_sequence"""}

          if self.use_past:
            self.fill_with_past_key_values_(lowerCamelCase							,  direction="""inputs"""   )
        elif self.task == "causal-lm":
          # TODO: figure this case out.
          snake_case_       						=      OrderedDict(
              [
                  ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                  ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
              ]   )
          if self.use_past:
            snake_case_						,				snake_case_       						=      self.num_layers
            for i in range(lowerCamelCase   ):
              snake_case_       						=      {0: """batch""", 2: """past_sequence + sequence"""}
              snake_case_       						=      {0: """batch""", 2: """past_sequence + sequence"""}
        else:
          snake_case_       						=      OrderedDict(
              [
                  ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                  ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
                  ("""decoder_input_ids""", {0: """batch""", 1: """decoder_sequence"""}),
                  ("""decoder_attention_mask""", {0: """batch""", 1: """decoder_sequence"""}),
              ]   )

        return common_inputs







      @property
      def 	lowerCAmelCase_    (     self   )						->       Mapping[str, Mapping[int, str]]:
        if self.task in ["default", "seq2seq-lm"]:
          snake_case_       						=      super().outputs
        else:
          snake_case_       						=      super(lowerCamelCase							,  self   ).outputs
          if self.use_past:
            snake_case_						,				snake_case_       						=      self.num_layers
            for i in range(lowerCamelCase   ):
              snake_case_       						=      {0: """batch""", 2: """past_sequence + sequence"""}
              snake_case_       						=      {0: """batch""", 2: """past_sequence + sequence"""}
        return common_outputs







      def 	lowerCAmelCase_    (     self							,  lowerCamelCase							,  lowerCamelCase = -1							,  lowerCamelCase = -1							,  lowerCamelCase = False							,  lowerCamelCase = None							,  )						->       Mapping[str, Any]:
        snake_case_       						=      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase   )

        # Generate decoder inputs
        snake_case_       						=      seq_length if not self.use_past else 1
        snake_case_       						=      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase   )
        snake_case_       						=      {f'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
        snake_case_       						=      dict(**lowerCamelCase							,  **lowerCamelCase   )

        if self.use_past:
          if not is_torch_available():
            raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""   )
          else:
            import torch
          snake_case_						,				snake_case_       						=      common_inputs["""input_ids"""].shape
          snake_case_       						=      common_inputs["""decoder_input_ids"""].shape[1]
          snake_case_						,				snake_case_       						=      self.num_attention_heads
          snake_case_       						=      (
              batch,
              num_encoder_attention_heads,
              encoder_seq_length,
              self._config.hidden_size // num_encoder_attention_heads,
          )
          snake_case_       						=      decoder_seq_length + 3
          snake_case_       						=      (
              batch,
              num_decoder_attention_heads,
              decoder_past_length,
              self._config.hidden_size // num_decoder_attention_heads,
          )

          snake_case_       						=      torch.cat(
              [common_inputs["""decoder_attention_mask"""], torch.ones(lowerCamelCase							,  lowerCamelCase   )]							,  dim=1   )

          snake_case_       						=      []
          # If the number of encoder and decoder layers are present in the model configuration, both are considered
          snake_case_						,				snake_case_       						=      self.num_layers
          snake_case_       						=      min(lowerCamelCase							,  lowerCamelCase   )
          snake_case_       						=      max(lowerCamelCase							,  lowerCamelCase   ) - min_num_layers
          snake_case_       						=      """encoder""" if num_encoder_layers > num_decoder_layers else """decoder"""

          for _ in range(lowerCamelCase   ):
            common_inputs["past_key_values"].append(
                (
                    torch.zeros(lowerCamelCase   ),
                    torch.zeros(lowerCamelCase   ),
                    torch.zeros(lowerCamelCase   ),
                    torch.zeros(lowerCamelCase   ),
                )   )
          # TODO: test this.
          snake_case_       						=      encoder_shape if remaining_side_name == """encoder""" else decoder_shape
          for _ in range(lowerCamelCase							,  lowerCamelCase   ):
            common_inputs["past_key_values"].append((torch.zeros(lowerCamelCase   ), torch.zeros(lowerCamelCase   ))   )
        return common_inputs







      def 	lowerCAmelCase_    (     self							,  lowerCamelCase							,  lowerCamelCase = -1							,  lowerCamelCase = -1							,  lowerCamelCase = False							,  lowerCamelCase = None							,  )						->       Mapping[str, Any]:
        snake_case_       						=      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase   )

        if self.use_past:
          if not is_torch_available():
            raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""   )
          else:
            import torch
          snake_case_						,				snake_case_       						=      common_inputs["""input_ids"""].shape
          # Not using the same length for past_key_values
          snake_case_       						=      seqlen + 2
          snake_case_						,				snake_case_       						=      self.num_layers
          snake_case_						,				snake_case_       						=      self.num_attention_heads
          snake_case_       						=      (
              batch,
              num_encoder_attention_heads,
              past_key_values_length,
              self._config.hidden_size // num_encoder_attention_heads,
          )

          snake_case_       						=      common_inputs["""attention_mask"""].dtype
          snake_case_       						=      torch.cat(
              [common_inputs["""attention_mask"""], torch.ones(lowerCamelCase							,  lowerCamelCase							,  dtype=lowerCamelCase   )]							,  dim=1   )
          snake_case_       						=      [
              (torch.zeros(lowerCamelCase   ), torch.zeros(lowerCamelCase   )) for _ in range(lowerCamelCase   )
          ]
        return common_inputs







      def 	lowerCAmelCase_    (     self							,  lowerCamelCase							,  lowerCamelCase = -1							,  lowerCamelCase = -1							,  lowerCamelCase = False							,  lowerCamelCase = None							,  )						->       Mapping[str, Any]:
        # Copied from OnnxConfig.generate_dummy_inputs
        # Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
        # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
        snake_case_       						=      compute_effective_axis_dimension(
            lowerCamelCase							,  fixed_dimension=OnnxConfig.default_fixed_batch							,  num_token_to_add=0   )

        # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
        snake_case_       						=      tokenizer.num_special_tokens_to_add(lowerCamelCase   )
        snake_case_       						=      compute_effective_axis_dimension(
            lowerCamelCase							,  fixed_dimension=OnnxConfig.default_fixed_sequence							,  num_token_to_add=lowerCamelCase   )

        # Generate dummy inputs according to compute batch and sequence
        snake_case_       						=      [""" """.join([tokenizer.unk_token]   ) * seq_length] * batch_size
        snake_case_       						=      dict(tokenizer(lowerCamelCase							,  return_tensors=lowerCamelCase   )   )
        return common_inputs







      def 	lowerCAmelCase_    (     self							,  lowerCamelCase							,  lowerCamelCase = -1							,  lowerCamelCase = -1							,  lowerCamelCase = False							,  lowerCamelCase = None							,  )						->       Mapping[str, Any]:
        if self.task in ["default", "seq2seq-lm"]:
          snake_case_       						=      self._generate_dummy_inputs_for_default_and_seqaseq_lm(
              lowerCamelCase							,  batch_size=lowerCamelCase							,  seq_length=lowerCamelCase							,  is_pair=lowerCamelCase							,  framework=lowerCamelCase   )

        elif self.task == "causal-lm":
          snake_case_       						=      self._generate_dummy_inputs_for_causal_lm(
              lowerCamelCase							,  batch_size=lowerCamelCase							,  seq_length=lowerCamelCase							,  is_pair=lowerCamelCase							,  framework=lowerCamelCase   )
        else:
          snake_case_       						=      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
              lowerCamelCase							,  batch_size=lowerCamelCase							,  seq_length=lowerCamelCase							,  is_pair=lowerCamelCase							,  framework=lowerCamelCase   )

        return common_inputs







      def 	lowerCAmelCase_    (     self							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase   )						->       Dict:
        if self.task in ["default", "seq2seq-lm"]:
          snake_case_       						=      super()._flatten_past_key_values_(lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase   )
        else:
          snake_case_       						=      super(lowerCamelCase							,  self   )._flatten_past_key_values_(
              lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase   )


import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest







class  __lowerCamelCase				(	__snake_case	):
      lowerCamelCase_	:      Tuple          =					(DDIMScheduler,)
      lowerCamelCase_	:      Union[str, Any]          =					(('eta', 0.0), ('num_inference_steps', 50))







      def 	lowerCAmelCase_    (     self							,  **lowerCamelCase   )						->       Dict:
        snake_case_       						=      {
            """num_train_timesteps""": 1000,
            """beta_start""": 0.0001,
            """beta_end""": 0.02,
            """beta_schedule""": """linear""",
            """clip_sample""": True,
        }

        config.update(**lowerCamelCase   )
        return config







      def 	lowerCAmelCase_    (     self							,  **lowerCamelCase   )						->       List[str]:
        snake_case_       						=      self.scheduler_classes[0]
        snake_case_       						=      self.get_scheduler_config(**lowerCamelCase   )
        snake_case_       						=      scheduler_class(**lowerCamelCase   )

        snake_case_						,				snake_case_       						=      10, 0.0

        snake_case_       						=      self.dummy_model()
        snake_case_       						=      self.dummy_sample_deter

        scheduler.set_timesteps(lowerCamelCase   )

        for t in scheduler.timesteps:
          snake_case_       						=      model(lowerCamelCase							,  lowerCamelCase   )
          snake_case_       						=      scheduler.step(lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase   ).prev_sample

        return sample







      def 	lowerCAmelCase_    (     self   )						->       List[Any]:
        for timesteps in [100, 500, 1000]:
          self.check_over_configs(num_train_timesteps=lowerCamelCase   )







      def 	lowerCAmelCase_    (     self   )						->       Optional[int]:
        for steps_offset in [0, 1]:
          self.check_over_configs(steps_offset=lowerCamelCase   )

        snake_case_       						=      self.scheduler_classes[0]
        snake_case_       						=      self.get_scheduler_config(steps_offset=1   )
        snake_case_       						=      scheduler_class(**lowerCamelCase   )
        scheduler.set_timesteps(5   )
        assert torch.equal(scheduler.timesteps							,  torch.LongTensor([801, 601, 401, 201, 1]   )   )







      def 	lowerCAmelCase_    (     self   )						->       Optional[Any]:
        for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]							,  [0.002, 0.02, 0.2, 2]   ):
          self.check_over_configs(beta_start=lowerCamelCase							,  beta_end=lowerCamelCase   )







      def 	lowerCAmelCase_    (     self   )						->       Dict:
        for schedule in ["linear", "squaredcos_cap_v2"]:
          self.check_over_configs(beta_schedule=lowerCamelCase   )







      def 	lowerCAmelCase_    (     self   )						->       Any:
        for prediction_type in ["epsilon", "v_prediction"]:
          self.check_over_configs(prediction_type=lowerCamelCase   )







      def 	lowerCAmelCase_    (     self   )						->       Union[str, Any]:
        for clip_sample in [True, False]:
          self.check_over_configs(clip_sample=lowerCamelCase   )







      def 	lowerCAmelCase_    (     self   )						->       Optional[Any]:
        for timestep_spacing in ["trailing", "leading"]:
          self.check_over_configs(timestep_spacing=lowerCamelCase   )







      def 	lowerCAmelCase_    (     self   )						->       Dict:
        for rescale_betas_zero_snr in [True, False]:
          self.check_over_configs(rescale_betas_zero_snr=lowerCamelCase   )







      def 	lowerCAmelCase_    (     self   )						->       Optional[Any]:
        self.check_over_configs(thresholding=lowerCamelCase   )
        for threshold in [0.5, 1.0, 2.0]:
          for prediction_type in ["epsilon", "v_prediction"]:
            self.check_over_configs(
                thresholding=lowerCamelCase							,  prediction_type=lowerCamelCase							,  sample_max_value=lowerCamelCase							,  )







      def 	lowerCAmelCase_    (     self   )						->       Dict:
        for t in [1, 10, 49]:
          self.check_over_forward(time_step=lowerCamelCase   )







      def 	lowerCAmelCase_    (     self   )						->       List[Any]:
        for t, num_inference_steps in zip([1, 10, 50]							,  [10, 50, 500]   ):
          self.check_over_forward(time_step=lowerCamelCase							,  num_inference_steps=lowerCamelCase   )







      def 	lowerCAmelCase_    (     self   )						->       Union[str, Any]:
        for t, eta in zip([1, 10, 49]							,  [0.0, 0.5, 1.0]   ):
          self.check_over_forward(time_step=lowerCamelCase							,  eta=lowerCamelCase   )







      def 	lowerCAmelCase_    (     self   )						->       int:
        snake_case_       						=      self.scheduler_classes[0]
        snake_case_       						=      self.get_scheduler_config()
        snake_case_       						=      scheduler_class(**lowerCamelCase   )

        assert torch.sum(torch.abs(scheduler._get_variance(0							,  0   ) - 0.0   )   ) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(420							,  400   ) - 0.1_4771   )   ) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(980							,  960   ) - 0.3_2460   )   ) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(0							,  0   ) - 0.0   )   ) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(487							,  486   ) - 0.0_0979   )   ) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(999							,  998   ) - 0.02   )   ) < 1e-5







      def 	lowerCAmelCase_    (     self   )						->       Union[str, Any]:
        snake_case_       						=      self.full_loop()

        snake_case_       						=      torch.sum(torch.abs(lowerCamelCase   )   )
        snake_case_       						=      torch.mean(torch.abs(lowerCamelCase   )   )

        assert abs(result_sum.item() - 172.0067   ) < 1e-2
        assert abs(result_mean.item() - 0.22_3967   ) < 1e-3







      def 	lowerCAmelCase_    (     self   )						->       Optional[Any]:
        snake_case_       						=      self.full_loop(prediction_type="""v_prediction"""   )

        snake_case_       						=      torch.sum(torch.abs(lowerCamelCase   )   )
        snake_case_       						=      torch.mean(torch.abs(lowerCamelCase   )   )

        assert abs(result_sum.item() - 52.5302   ) < 1e-2
        assert abs(result_mean.item() - 0.0684   ) < 1e-3







      def 	lowerCAmelCase_    (     self   )						->       Optional[int]:
        # We specify different beta, so that the first alpha is 0.99
        snake_case_       						=      self.full_loop(set_alpha_to_one=lowerCamelCase							,  beta_start=0.01   )
        snake_case_       						=      torch.sum(torch.abs(lowerCamelCase   )   )
        snake_case_       						=      torch.mean(torch.abs(lowerCamelCase   )   )

        assert abs(result_sum.item() - 149.8295   ) < 1e-2
        assert abs(result_mean.item() - 0.1951   ) < 1e-3







      def 	lowerCAmelCase_    (     self   )						->       List[Any]:
        # We specify different beta, so that the first alpha is 0.99
        snake_case_       						=      self.full_loop(set_alpha_to_one=lowerCamelCase							,  beta_start=0.01   )
        snake_case_       						=      torch.sum(torch.abs(lowerCamelCase   )   )
        snake_case_       						=      torch.mean(torch.abs(lowerCamelCase   )   )

        assert abs(result_sum.item() - 149.0784   ) < 1e-2
        assert abs(result_mean.item() - 0.1941   ) < 1e-3