



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


a__      =		logging.get_logger(__name__)

a__      =		{
    """google/bigbird-pegasus-large-arxiv""": (
        """https://huggingface.co/google/bigbird-pegasus-large-arxiv/resolve/main/config.json"""
    ),
    """google/bigbird-pegasus-large-pubmed""": (
        """https://huggingface.co/google/bigbird-pegasus-large-pubmed/resolve/main/config.json"""
    ),
    """google/bigbird-pegasus-large-bigpatent""": (
        """https://huggingface.co/google/bigbird-pegasus-large-bigpatent/resolve/main/config.json"""
    ),
    # See all BigBirdPegasus models at https://huggingface.co/models?filter=bigbird_pegasus
}







class 						snake_case  (		SCREAMING_SNAKE_CASE_					):



   '''simple docstring'''






   snake_case_   :	Any       =						"""bigbird_pegasus"""
   snake_case_   :	str       =						["""past_key_values"""]
   snake_case_   :	List[Any]       =						{
       """num_attention_heads""": """encoder_attention_heads""",
       """hidden_size""": """d_model""",
       """attention_probs_dropout_prob""": """attention_dropout""",
   }

   def __init__(   self							:     Optional[Any]			,						lowerCAmelCase							:     List[Any]=9_6103			,						lowerCAmelCase							:     Union[str, Any]=4096			,						lowerCAmelCase							:     List[str]=16			,						lowerCAmelCase							:     Tuple=4096			,						lowerCAmelCase							:     int=16			,						lowerCAmelCase							:     Union[str, Any]=16			,						lowerCAmelCase							:     Any=4096			,						lowerCAmelCase							:     Optional[Any]=16			,						lowerCAmelCase							:     Optional[int]=0.0			,						lowerCAmelCase							:     Any=0.0			,						lowerCAmelCase							:     str=True			,						lowerCAmelCase							:     Optional[Any]=True			,						lowerCAmelCase							:     Optional[Any]="gelu_new"			,						lowerCAmelCase							:     Optional[Any]=1024			,						lowerCAmelCase							:     Optional[Any]=0.1			,						lowerCAmelCase							:     List[Any]=0.0			,						lowerCAmelCase							:     List[str]=0.0			,						lowerCAmelCase							:     Any=0.02			,						lowerCAmelCase							:     Optional[int]=2			,						lowerCAmelCase							:     Dict=0.0			,						lowerCAmelCase							:     Optional[int]=True			,						lowerCAmelCase							:     List[Any]=0			,						lowerCAmelCase							:     str=2			,						lowerCAmelCase							:     Tuple=1			,						lowerCAmelCase							:     str="block_sparse"			,						lowerCAmelCase							:     int=64			,						lowerCAmelCase							:     str=3			,						lowerCAmelCase							:     Optional[int]=False			,						**lowerCAmelCase							:     List[Any]			,						)			->			Optional[int]:

    """simple docstring"""







    _snake_case      :					Dict					=   vocab_size
    _snake_case      :					Union[str, Any]					=   max_position_embeddings
    _snake_case      :					Union[str, Any]					=   d_model
    _snake_case      :					Optional[int]					=   encoder_ffn_dim
    _snake_case      :					List[str]					=   encoder_layers
    _snake_case      :					Dict					=   encoder_attention_heads
    _snake_case      :					Optional[Any]					=   decoder_ffn_dim
    _snake_case      :					Tuple					=   decoder_layers
    _snake_case      :					List[str]					=   decoder_attention_heads
    _snake_case      :					Dict					=   dropout
    _snake_case      :					Any					=   attention_dropout
    _snake_case      :					int					=   activation_dropout
    _snake_case      :					str					=   activation_function
    _snake_case      :					Dict					=   init_std
    _snake_case      :					List[Any]					=   encoder_layerdrop
    _snake_case      :					int					=   decoder_layerdrop
    _snake_case      :					List[Any]					=   classifier_dropout
    _snake_case      :					Optional[int]					=   use_cache
    _snake_case      :					Optional[int]					=   encoder_layers
    _snake_case      :					Optional[Any]					=   scale_embedding  # scale factor will be sqrt(d_model) if True

    # extra config
    _snake_case      :					Optional[Any]					=   attention_type
    _snake_case      :					str					=   block_size
    _snake_case      :					List[Any]					=   num_random_blocks
    _snake_case      :					List[str]					=   use_bias

    super().__init__(
        pad_token_id=lowerCAmelCase			,						bos_token_id=lowerCAmelCase			,						eos_token_id=lowerCAmelCase			,						is_encoder_decoder=lowerCAmelCase			,						decoder_start_token_id=lowerCAmelCase			,						**lowerCAmelCase			,						)







class 						snake_case  (		SCREAMING_SNAKE_CASE_					):



   '''simple docstring'''






   @property
   def        UpperCamelCase_					(   self							:     int)			->			Mapping[str, Mapping[int, str]]:

    """simple docstring"""







    if self.task in ["default", "seq2seq-lm"]:
     _snake_case      :					Union[str, Any]					=   OrderedDict(
         [
             ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
             ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
         ])

     if self.use_past:
      _snake_case      :					Optional[int]					=   {0: """batch"""}
      _snake_case      :					Optional[Any]					=   {0: """batch""", 1: """past_decoder_sequence + sequence"""}
     else:
      _snake_case      :					List[Any]					=   {0: """batch""", 1: """decoder_sequence"""}
      _snake_case      :					str					=   {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      :					Dict					=   OrderedDict(
         [
             ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
             ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
         ])
     if self.use_past:
      _snake_case		,      _snake_case      :					Dict					=   self.num_layers
      for i in range(lowerCAmelCase):
       _snake_case      :					str					=   {0: """batch""", 2: """past_sequence + sequence"""}
       _snake_case      :					Tuple					=   {0: """batch""", 2: """past_sequence + sequence"""}
    else:
     _snake_case      :					str					=   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        UpperCamelCase_					(   self							:     str)			->			Mapping[str, Mapping[int, str]]:

    """simple docstring"""







    if self.task in ["default", "seq2seq-lm"]:
     _snake_case      :					Optional[int]					=   super().outputs
    else:
     _snake_case      :					List[str]					=   super(lowerCAmelCase			,						self).outputs
     if self.use_past:
      _snake_case		,      _snake_case      :					Dict					=   self.num_layers
      for i in range(lowerCAmelCase):
       _snake_case      :					Optional[Any]					=   {0: """batch""", 2: """past_sequence + sequence"""}
       _snake_case      :					List[str]					=   {0: """batch""", 2: """past_sequence + sequence"""}
    return common_outputs

   def        UpperCamelCase_					(   self							:     Any			,						lowerCAmelCase							:     PreTrainedTokenizer			,						lowerCAmelCase							:     int = -1			,						lowerCAmelCase							:     int = -1			,						lowerCAmelCase							:     bool = False			,						lowerCAmelCase							:     Optional[TensorType] = None			,						)			->			Mapping[str, Any]:

    """simple docstring"""







    _snake_case      :					Union[str, Any]					=   self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
        lowerCAmelCase			,						lowerCAmelCase			,						lowerCAmelCase			,						lowerCAmelCase			,						lowerCAmelCase)

    # Generate decoder inputs
    _snake_case      :					str					=   seq_length if not self.use_past else 1
    _snake_case      :					int					=   self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
        lowerCAmelCase			,						lowerCAmelCase			,						lowerCAmelCase			,						lowerCAmelCase			,						lowerCAmelCase)
    _snake_case      :					Optional[Any]					=   {F'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
    _snake_case      :					Union[str, Any]					=   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      :					Any					=   common_inputs["""input_ids"""].shape
     _snake_case      :					Union[str, Any]					=   common_inputs["""decoder_input_ids"""].shape[1]
     _snake_case		,      _snake_case      :					List[Any]					=   self.num_attention_heads
     _snake_case      :					Tuple					=   (
         batch,
         num_encoder_attention_heads,
         encoder_seq_length,
         self._config.hidden_size // num_encoder_attention_heads,
     )
     _snake_case      :					str					=   decoder_seq_length + 3
     _snake_case      :					Tuple					=   (
         batch,
         num_decoder_attention_heads,
         decoder_past_length,
         self._config.hidden_size // num_decoder_attention_heads,
     )

     _snake_case      :					Optional[Any]					=   torch.cat(
         [common_inputs["""decoder_attention_mask"""], torch.ones(lowerCAmelCase			,						lowerCAmelCase)]			,						dim=1)

     _snake_case      :					Optional[int]					=   []
     # If the number of encoder and decoder layers are present in the model configuration, both are considered
     _snake_case		,      _snake_case      :					Optional[int]					=   self.num_layers
     _snake_case      :					Union[str, Any]					=   min(lowerCAmelCase			,						lowerCAmelCase)
     _snake_case      :					Any					=   max(lowerCAmelCase			,						lowerCAmelCase) - min_num_layers
     _snake_case      :					int					=   """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      :					Optional[int]					=   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        UpperCamelCase_					(   self							:     Optional[int]			,						lowerCAmelCase							:     PreTrainedTokenizer			,						lowerCAmelCase							:     int = -1			,						lowerCAmelCase							:     int = -1			,						lowerCAmelCase							:     bool = False			,						lowerCAmelCase							:     Optional[TensorType] = None			,						)			->			Mapping[str, Any]:

    """simple docstring"""







    _snake_case      :					List[str]					=   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      :					List[Any]					=   common_inputs["""input_ids"""].shape
     # Not using the same length for past_key_values
     _snake_case      :					Optional[int]					=   seqlen + 2
     _snake_case		,      _snake_case      :					str					=   self.num_layers
     _snake_case		,      _snake_case      :					int					=   self.num_attention_heads
     _snake_case      :					str					=   (
         batch,
         num_encoder_attention_heads,
         past_key_values_length,
         self._config.hidden_size // num_encoder_attention_heads,
     )

     _snake_case      :					List[Any]					=   common_inputs["""attention_mask"""].dtype
     _snake_case      :					Tuple					=   torch.cat(
         [common_inputs["""attention_mask"""], torch.ones(lowerCAmelCase			,						lowerCAmelCase			,						dtype=lowerCAmelCase)]			,						dim=1)
     _snake_case      :					Dict					=   [
         (torch.zeros(lowerCAmelCase), torch.zeros(lowerCAmelCase)) for _ in range(lowerCAmelCase)
     ]
    return common_inputs

   def        UpperCamelCase_					(   self							:     Tuple			,						lowerCAmelCase							:     PreTrainedTokenizer			,						lowerCAmelCase							:     int = -1			,						lowerCAmelCase							:     int = -1			,						lowerCAmelCase							:     bool = False			,						lowerCAmelCase							:     Optional[TensorType] = None			,						)			->			Mapping[str, Any]:

    """simple docstring"""







    _snake_case      :					List[str]					=   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      :					int					=   tokenizer.num_special_tokens_to_add(lowerCAmelCase)
    _snake_case      :					List[str]					=   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      :					List[Any]					=   [""" """.join([tokenizer.unk_token]) * seq_length] * batch_size
    _snake_case      :					Optional[Any]					=   dict(tokenizer(lowerCAmelCase			,						return_tensors=lowerCAmelCase))
    return common_inputs

   def        UpperCamelCase_					(   self							:     List[Any]			,						lowerCAmelCase							:     PreTrainedTokenizer			,						lowerCAmelCase							:     int = -1			,						lowerCAmelCase							:     int = -1			,						lowerCAmelCase							:     bool = False			,						lowerCAmelCase							:     Optional[TensorType] = None			,						)			->			Mapping[str, Any]:

    """simple docstring"""







    if self.task in ["default", "seq2seq-lm"]:
     _snake_case      :					Union[str, Any]					=   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      :					Tuple					=   self._generate_dummy_inputs_for_causal_lm(
         lowerCAmelCase			,						batch_size=lowerCAmelCase			,						seq_length=lowerCAmelCase			,						is_pair=lowerCAmelCase			,						framework=lowerCAmelCase)
    else:
     _snake_case      :					Union[str, Any]					=   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        UpperCamelCase_					(   self							:     Any			,						lowerCAmelCase							:     Optional[int]			,						lowerCAmelCase							:     Tuple			,						lowerCAmelCase							:     List[str]			,						lowerCAmelCase							:     List[str])			->			Optional[Any]:

    """simple docstring"""







    if self.task in ["default", "seq2seq-lm"]:
     _snake_case      :					int					=   super()._flatten_past_key_values_(lowerCAmelCase			,						lowerCAmelCase			,						lowerCAmelCase			,						lowerCAmelCase)
    else:
     _snake_case      :					int					=   super(lowerCAmelCase			,						self)._flatten_past_key_values_(
         lowerCAmelCase			,						lowerCAmelCase			,						lowerCAmelCase			,						lowerCAmelCase)





import unittest

import numpy as np
import torch

from diffusers import DDPMPipeline, DDPMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device


enable_full_determinism()







class 						snake_case  (		unittest.TestCase					):



   '''simple docstring'''






   @property
   def        UpperCamelCase_					(   self							:     Union[str, Any])			->			int:

    """simple docstring"""







    torch.manual_seed(0)
    _snake_case      :					Any					=   UNetaDModel(
        block_out_channels=(32, 64)			,						layers_per_block=2			,						sample_size=32			,						in_channels=3			,						out_channels=3			,						down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")			,						up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")			,						)
    return model

   def        UpperCamelCase_					(   self							:     List[Any])			->			Optional[Any]:

    """simple docstring"""







    _snake_case      :					List[str]					=   """cpu"""
    _snake_case      :					Union[str, Any]					=   self.dummy_uncond_unet
    _snake_case      :					Union[str, Any]					=   DDPMScheduler()

    _snake_case      :					Dict					=   DDPMPipeline(unet=lowerCAmelCase			,						scheduler=lowerCAmelCase)
    ddpm.to(lowerCAmelCase)
    ddpm.set_progress_bar_config(disable=lowerCAmelCase)

    _snake_case      :					Optional[Any]					=   torch.Generator(device=lowerCAmelCase).manual_seed(0)
    _snake_case      :					Optional[int]					=   ddpm(generator=lowerCAmelCase			,						num_inference_steps=2			,						output_type="""numpy""").images

    _snake_case      :					Optional[int]					=   torch.Generator(device=lowerCAmelCase).manual_seed(0)
    _snake_case      :					Union[str, Any]					=   ddpm(generator=lowerCAmelCase			,						num_inference_steps=2			,						output_type="""numpy"""			,						return_dict=lowerCAmelCase)[0]

    _snake_case      :					Any					=   image[0, -3:, -3:, -1]
    _snake_case      :					str					=   image_from_tuple[0, -3:, -3:, -1]

    assert image.shape == (1, 32, 32, 3)
    _snake_case      :					Optional[int]					=   np.array(
        [9.956E-01, 5.785E-01, 4.675E-01, 9.930E-01, 0.0, 1.000, 1.199E-03, 2.648E-04, 5.101E-04])

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


   def        UpperCamelCase_					(   self							:     List[Any])			->			Optional[int]:

    """simple docstring"""







    _snake_case      :					int					=   self.dummy_uncond_unet
    _snake_case      :					int					=   DDPMScheduler(prediction_type="""sample""")

    _snake_case      :					Union[str, Any]					=   DDPMPipeline(unet=lowerCAmelCase			,						scheduler=lowerCAmelCase)
    ddpm.to(lowerCAmelCase)
    ddpm.set_progress_bar_config(disable=lowerCAmelCase)

    _snake_case      :					Tuple					=   torch.manual_seed(0)
    _snake_case      :					Union[str, Any]					=   ddpm(generator=lowerCAmelCase			,						num_inference_steps=2			,						output_type="""numpy""").images

    _snake_case      :					Any					=   torch.manual_seed(0)
    _snake_case      :					Dict					=   ddpm(generator=lowerCAmelCase			,						num_inference_steps=2			,						output_type="""numpy""")[0]

    _snake_case      :					str					=   image[0, -3:, -3:, -1]
    _snake_case      :					List[Any]					=   image_eps[0, -3:, -3:, -1]

    assert image.shape == (1, 32, 32, 3)
    _snake_case      :					Any					=   1E-2 if torch_device != """mps""" else 3E-2
    assert np.abs(image_slice.flatten() - image_eps_slice.flatten()).max() < tolerance







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



   '''simple docstring'''






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

    """simple docstring"""







    _snake_case      :					Dict					=   """google/ddpm-cifar10-32"""

    _snake_case      :					List[Any]					=   UNetaDModel.from_pretrained(lowerCAmelCase)
    _snake_case      :					Optional[Any]					=   DDPMScheduler.from_pretrained(lowerCAmelCase)

    _snake_case      :					Dict					=   DDPMPipeline(unet=lowerCAmelCase			,						scheduler=lowerCAmelCase)
    ddpm.to(lowerCAmelCase)
    ddpm.set_progress_bar_config(disable=lowerCAmelCase)

    _snake_case      :					Optional[Any]					=   torch.manual_seed(0)
    _snake_case      :					Any					=   ddpm(generator=lowerCAmelCase			,						output_type="""numpy""").images

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

    assert image.shape == (1, 32, 32, 3)
    _snake_case      :					Union[str, Any]					=   np.array([0.4_200, 0.3_588, 0.1_939, 0.3_847, 0.3_382, 0.2_647, 0.4_155, 0.3_582, 0.3_385])
    assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2
