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

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


UpperCAmelCase_           =				logging.get_logger(__name__)

UpperCAmelCase_           =				{
    '''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/config.json''',
    # See all BlenderbotSmall models at https://huggingface.co/models?filter=blenderbot_small
}




class 			__magic_name__	(		__a     ):





   """simple docstring"""




   lowerCAmelCase   :     List[str]	      = '''blenderbot-small'''
   lowerCAmelCase   :     List[Any]	      = ['''past_key_values''']
   lowerCAmelCase   :     int	      = {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}



   def __init__(						self       :	str		,	_lowercase       :	List[str]=50_265		,	_lowercase       :	Any=512		,	_lowercase       :	int=8		,	_lowercase       :	List[str]=2_048		,	_lowercase       :	Dict=16		,	_lowercase       :	Optional[Any]=8		,	_lowercase       :	List[str]=2_048		,	_lowercase       :	Optional[int]=16		,	_lowercase       :	Dict=0.0		,	_lowercase       :	int=0.0		,	_lowercase       :	Optional[Any]=True		,	_lowercase       :	Dict=True		,	_lowercase       :	Dict="gelu"		,	_lowercase       :	List[str]=512		,	_lowercase       :	int=0.1		,	_lowercase       :	str=0.0		,	_lowercase       :	Dict=0.0		,	_lowercase       :	int=0.02		,	_lowercase       :	Optional[int]=1		,	_lowercase       :	Optional[Any]=False		,	_lowercase       :	Optional[Any]=0		,	_lowercase       :	List[Any]=1		,	_lowercase       :	Any=2		,	_lowercase       :	Tuple=2		,	**_lowercase       :	Dict		,	):



    """simple docstring"""



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

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




class 			__magic_name__	(		__a     ):





   """simple docstring"""




   @property
   def 	lowerCAmelCase    (						self       :	List[Any]				):



    """simple docstring"""



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

     if self.use_past:
      _UpperCamelCase:  Tuple    						=			{0: '''batch'''}
      _UpperCamelCase:  str    						=			{0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
     else:
      _UpperCamelCase:  Optional[Any]    						=			{0: '''batch''', 1: '''decoder_sequence'''}
      _UpperCamelCase:  Optional[Any]    						=			{0: '''batch''', 1: '''decoder_sequence'''}

     if self.use_past:
      self.fill_with_past_key_values_(_lowercase		,	direction='''inputs'''				)
    elif self.task == "causal-lm":
     # TODO: figure this case out.
     _UpperCamelCase:  List[Any]    						=			OrderedDict(
         [
             ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
             ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
         ]				)
     if self.use_past:
      _UpperCamelCase						,				_UpperCamelCase:  Dict    						=			self.num_layers
      for i in range(_lowercase				):
       _UpperCamelCase:  Optional[Any]    						=			{0: '''batch''', 2: '''past_sequence + sequence'''}
       _UpperCamelCase:  Dict    						=			{0: '''batch''', 2: '''past_sequence + sequence'''}
    else:
     _UpperCamelCase:  Dict    						=			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       :	Dict				):



    """simple docstring"""



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



   def 	lowerCAmelCase    (						self       :	Tuple		,	_lowercase       :	PreTrainedTokenizer		,	_lowercase       :	int = -1		,	_lowercase       :	int = -1		,	_lowercase       :	bool = False		,	_lowercase       :	Optional[TensorType] = None		,	):



    """simple docstring"""



    _UpperCamelCase:  str    						=			self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
        _lowercase		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase				)

    # Generate decoder inputs
    _UpperCamelCase:  Union[str, Any]    						=			seq_length if not self.use_past else 1
    _UpperCamelCase:  List[Any]    						=			self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
        _lowercase		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase				)
    _UpperCamelCase:  Dict    						=			{f"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
    _UpperCamelCase:  int    						=			dict(**_lowercase		,	**_lowercase				)

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

     _UpperCamelCase:  Dict    						=			torch.cat(
         [common_inputs['''decoder_attention_mask'''], torch.ones(_lowercase		,	_lowercase				)]		,	dim=1				)

     _UpperCamelCase:  int    						=			[]
     # If the number of encoder and decoder layers are present in the model configuration, both are considered
     _UpperCamelCase						,				_UpperCamelCase:  Optional[Any]    						=			self.num_layers
     _UpperCamelCase:  List[Any]    						=			min(_lowercase		,	_lowercase				)
     _UpperCamelCase:  Optional[Any]    						=			max(_lowercase		,	_lowercase				) - min_num_layers
     _UpperCamelCase:  List[Any]    						=			'''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''

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



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



    """simple docstring"""



    _UpperCamelCase:  str    						=			self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
        _lowercase		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase				)

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

     _UpperCamelCase:  Tuple    						=			common_inputs['''attention_mask'''].dtype
     _UpperCamelCase:  Union[str, Any]    						=			torch.cat(
         [common_inputs['''attention_mask'''], torch.ones(_lowercase		,	_lowercase		,	dtype=_lowercase				)]		,	dim=1				)
     _UpperCamelCase:  Optional[Any]    						=			[
         (torch.zeros(_lowercase				), torch.zeros(_lowercase				)) for _ in range(_lowercase				)
     ]
    return common_inputs



   def 	lowerCAmelCase    (						self       :	Tuple		,	_lowercase       :	PreTrainedTokenizer		,	_lowercase       :	int = -1		,	_lowercase       :	int = -1		,	_lowercase       :	bool = False		,	_lowercase       :	Optional[TensorType] = None		,	):



    """simple docstring"""



    _UpperCamelCase:  Optional[Any]    						=			compute_effective_axis_dimension(
        _lowercase		,	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
    _UpperCamelCase:  List[Any]    						=			tokenizer.num_special_tokens_to_add(_lowercase				)
    _UpperCamelCase:  Union[str, Any]    						=			compute_effective_axis_dimension(
        _lowercase		,	fixed_dimension=OnnxConfig.default_fixed_sequence		,	num_token_to_add=_lowercase				)

    # Generate dummy inputs according to compute batch and sequence
    _UpperCamelCase:  int    						=			[''' '''.join([tokenizer.unk_token]				) * seq_length] * batch_size
    _UpperCamelCase:  Optional[int]    						=			dict(tokenizer(_lowercase		,	return_tensors=_lowercase				)				)
    return common_inputs



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



    """simple docstring"""



    if self.task in ["default", "seq2seq-lm"]:
     _UpperCamelCase:  List[str]    						=			self._generate_dummy_inputs_for_default_and_seqaseq_lm(
         _lowercase		,	batch_size=_lowercase		,	seq_length=_lowercase		,	is_pair=_lowercase		,	framework=_lowercase				)

    elif self.task == "causal-lm":
     _UpperCamelCase:  Optional[Any]    						=			self._generate_dummy_inputs_for_causal_lm(
         _lowercase		,	batch_size=_lowercase		,	seq_length=_lowercase		,	is_pair=_lowercase		,	framework=_lowercase				)
    else:
     _UpperCamelCase:  Dict    						=			self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
         _lowercase		,	batch_size=_lowercase		,	seq_length=_lowercase		,	is_pair=_lowercase		,	framework=_lowercase				)

    return common_inputs



   def 	lowerCAmelCase    (						self       :	Dict		,	_lowercase       :	str		,	_lowercase       :	Optional[int]		,	_lowercase       :	Optional[Any]		,	_lowercase       :	Optional[int]				):



    """simple docstring"""



    if self.task in ["default", "seq2seq-lm"]:
     _UpperCamelCase:  Any    						=			super()._flatten_past_key_values_(_lowercase		,	_lowercase		,	_lowercase		,	_lowercase				)
    else:
     _UpperCamelCase:  Dict    						=			super(_lowercase		,	self				)._flatten_past_key_values_(
         _lowercase		,	_lowercase		,	_lowercase		,	_lowercase				)
import unittest

import torch

from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()




class 			__magic_name__	(		__a   ,						__a   ,						unittest.TestCase     ):





   """simple docstring"""




   lowerCAmelCase   :     Dict	      = VQModel
   lowerCAmelCase   :     List[str]	      = '''sample'''



   @property
   def 	lowerCAmelCase    (						self       :	List[Any]		,	_lowercase       :	Tuple=(32, 32)				):



    """simple docstring"""



    _UpperCamelCase:  List[Any]    						=			4
    _UpperCamelCase:  Tuple    						=			3

    _UpperCamelCase:  List[str]    						=			floats_tensor((batch_size, num_channels) + sizes				).to(_lowercase				)

    return {"sample": image}



   @property
   def 	lowerCAmelCase    (						self       :	List[Any]				):



    """simple docstring"""



    return (3, 32, 32)



   @property
   def 	lowerCAmelCase    (						self       :	str				):



    """simple docstring"""



    return (3, 32, 32)



   def 	lowerCAmelCase    (						self       :	Optional[int]				):



    """simple docstring"""



    _UpperCamelCase:  Any    						=			{
        '''block_out_channels''': [32, 64],
        '''in_channels''': 3,
        '''out_channels''': 3,
        '''down_block_types''': ['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''],
        '''up_block_types''': ['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''],
        '''latent_channels''': 3,
    }
    _UpperCamelCase:  Optional[Any]    						=			self.dummy_input
    return init_dict, inputs_dict



   def 	lowerCAmelCase    (						self       :	List[Any]				):



    """simple docstring"""



    pass



   def 	lowerCAmelCase    (						self       :	List[Any]				):



    """simple docstring"""



    pass



   def 	lowerCAmelCase    (						self       :	Optional[Any]				):



    """simple docstring"""



    _UpperCamelCase						,				_UpperCamelCase:  int    						=			VQModel.from_pretrained('''fusing/vqgan-dummy'''		,	output_loading_info=_lowercase				)
    self.assertIsNotNone(_lowercase				)
    self.assertEqual(len(loading_info['''missing_keys''']				)		,	0				)

    model.to(_lowercase				)
    _UpperCamelCase:  Tuple    						=			model(**self.dummy_input				)

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



   def 	lowerCAmelCase    (						self       :	Optional[Any]				):



    """simple docstring"""



    _UpperCamelCase:  Dict    						=			VQModel.from_pretrained('''fusing/vqgan-dummy'''				)
    model.to(_lowercase				).eval()

    torch.manual_seed(0				)
    if torch.cuda.is_available():
     torch.cuda.manual_seed_all(0				)

    _UpperCamelCase:  int    						=			torch.randn(1		,	model.config.in_channels		,	model.config.sample_size		,	model.config.sample_size				)
    _UpperCamelCase:  Tuple    						=			image.to(_lowercase				)
    with torch.no_grad():
     _UpperCamelCase:  Optional[int]    						=			model(_lowercase				).sample

    _UpperCamelCase:  List[Any]    						=			output[0, -1, -3:, -3:].flatten().cpu()
    # fmt: off
    _UpperCamelCase:  Any    						=			torch.tensor([-0.0153, -0.4044, -0.1880, -0.5161, -0.2418, -0.4072, -0.1612, -0.0633, -0.0143]				)
    # fmt: on
    self.assertTrue(torch.allclose(_lowercase		,	_lowercase		,	atol=1E-3				)				)