
'''simple docstring'''







import inspect

import jax
import jax.lax as lax
import jax.numpy as jnp

from ..utils import add_start_docstrings
from ..utils.logging import get_logger


_lowercase :  Tuple    							=       get_logger(__name__)


_lowercase :  Union[str, Any]    							=       r"\n    Args:\n        input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`):\n            Indices of input sequence tokens in the vocabulary.\n\n            Indices can be obtained using [`PreTrainedTokenizer`]. See [`PreTrainedTokenizer.encode`] and\n            [`PreTrainedTokenizer.__call__`] for details.\n\n            [What are input IDs?](../glossary#input-ids)\n        scores (`jnp.ndarray` of shape `(batch_size, config.vocab_size)`):\n            Prediction scores of a language modeling head. These can be logits for each vocabulary when not using beam\n            search or log softmax for each vocabulary token when using beam search\n        kwargs (`Dict[str, Any]`, *optional*):\n            Additional logits processor specific kwargs.\n\n    Return:\n        `jnp.ndarray` of shape `(batch_size, config.vocab_size)`: The processed prediction scores.\n\n"






class  lowerCAmelCase__   :






					@add_start_docstrings(__SCREAMING_SNAKE_CASE		)
					def __call__(   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											raise NotImplementedError(
											    F'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.'''		)







class  lowerCAmelCase__   :






					@add_start_docstrings(__SCREAMING_SNAKE_CASE		)
					def __call__(   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											raise NotImplementedError(
											    F'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.'''		)







class  lowerCAmelCase__   (  lowerCamelCase_     ):






					@add_start_docstrings(__SCREAMING_SNAKE_CASE		)
					def __call__(   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							**__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											for processor in self:
																	lowercase_	:     Dict													=       inspect.signature(processor.__call__		).parameters
																	if len(__SCREAMING_SNAKE_CASE		) > 3:
																							if not all(arg in kwargs for arg in list(function_args.keys()		)[2:]		):
																													raise ValueError(
																													    F'''Make sure that all the required parameters: {list(function_args.keys()		)} for '''
																													    F'''{processor.__class__} are passed to the logits processor.'''		)
																							lowercase_	:     Dict													=       processor(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							**__SCREAMING_SNAKE_CASE		)
																	else:
																							lowercase_	:     Dict													=       processor(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		)
											return scores







class  lowerCAmelCase__   (  lowerCamelCase_     ):






					def __init__(   self				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											if not isinstance(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		) or not (temperature > 0):
																	raise ValueError(F'''`temperature` has to be a strictly positive float, but is {temperature}'''		)

											lowercase_	:     int													=       temperature






					def __call__(   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_	:     Tuple													=       scores / self.temperature
											return scores







class  lowerCAmelCase__   (  lowerCamelCase_     ):






					def __init__(   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE = -float('''Inf'''		)				,							__SCREAMING_SNAKE_CASE = 1		):


											"""simple docstring"""





											if not isinstance(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		) or (top_p < 0 or top_p > 1.0):
																	raise ValueError(F'''`top_p` has to be a float > 0 and < 1, but is {top_p}'''		)
											if not isinstance(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		) or (min_tokens_to_keep < 1):
																	raise ValueError(F'''`min_tokens_to_keep` has to be a positive integer, but is {min_tokens_to_keep}'''		)

											lowercase_	:     Optional[int]													=       top_p
											lowercase_	:     Optional[Any]													=       filter_value
											lowercase_	:     Optional[Any]													=       min_tokens_to_keep






					def __call__(   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_						, lowercase_	:     Optional[int]													=       lax.top_k(__SCREAMING_SNAKE_CASE				,							scores.shape[-1]		)

											lowercase_	:     List[str]													=       jnp.full_like(__SCREAMING_SNAKE_CASE				,							self.filter_value		)
											lowercase_	:     List[Any]													=       jax.nn.softmax(__SCREAMING_SNAKE_CASE				,							axis=-1		).cumsum(axis=-1		)
											lowercase_	:     Dict													=       cumulative_probs < self.top_p

											# include the token that is higher than top_p as well
											lowercase_	:     List[Any]													=       jnp.roll(__SCREAMING_SNAKE_CASE				,							1		)
											score_mask |= score_mask.at[:, 0].set(__SCREAMING_SNAKE_CASE		)

											# min tokens to keep
											lowercase_	:     List[str]													=       score_mask.at[:, : self.min_tokens_to_keep].set(__SCREAMING_SNAKE_CASE		)

											lowercase_	:     Any													=       jnp.where(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		)
											lowercase_	:     str													=       jax.lax.sort_key_val(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		)[-1]

											return next_scores







class  lowerCAmelCase__   (  lowerCamelCase_     ):






					def __init__(   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE = -float('''Inf'''		)				,							__SCREAMING_SNAKE_CASE = 1		):


											"""simple docstring"""





											if not isinstance(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		) or top_k <= 0:
																	raise ValueError(F'''`top_k` has to be a strictly positive integer, but is {top_k}'''		)

											lowercase_	:     str													=       max(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Optional[Any]													=       filter_value






					def __call__(   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_						, lowercase_	:     Union[str, Any]													=       scores.shape
											lowercase_	:     int													=       jnp.full(batch_size * vocab_size				,							self.filter_value		)

											lowercase_	:     List[Any]													=       min(self.top_k				,							scores.shape[-1]		)  # Safety check
											lowercase_						, lowercase_	:     int													=       lax.top_k(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Dict													=       jnp.broadcast_to((jnp.arange(__SCREAMING_SNAKE_CASE		) * vocab_size)[:, None]				,							(batch_size, topk)		).flatten()
											lowercase_	:     int													=       topk_scores.flatten()
											lowercase_	:     Tuple													=       topk_indices.flatten() + shift

											lowercase_	:     str													=       next_scores_flat.at[topk_indices_flat].set(__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Optional[Any]													=       next_scores_flat.reshape(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		)
											return next_scores







class  lowerCAmelCase__   (  lowerCamelCase_     ):






					def __init__(   self				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_	:     Dict													=       bos_token_id






					def __call__(   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_	:     Optional[Any]													=       jnp.full(scores.shape				,							-float('''inf'''		)		)

											lowercase_	:     Union[str, Any]													=       1 - jnp.bool_(cur_len - 1		)

											lowercase_	:     Optional[int]													=       jnp.where(__SCREAMING_SNAKE_CASE				,							new_scores.at[:, self.bos_token_id].set(0		)				,							__SCREAMING_SNAKE_CASE		)

											return scores







class  lowerCAmelCase__   (  lowerCamelCase_     ):






					def __init__(   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_	:     Any													=       max_length
											lowercase_	:     Union[str, Any]													=       eos_token_id






					def __call__(   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_	:     Optional[int]													=       jnp.full(scores.shape				,							-float('''inf'''		)		)

											lowercase_	:     List[str]													=       1 - jnp.bool_(cur_len - self.max_length + 1		)

											lowercase_	:     Union[str, Any]													=       jnp.where(__SCREAMING_SNAKE_CASE				,							new_scores.at[:, self.eos_token_id].set(0		)				,							__SCREAMING_SNAKE_CASE		)

											return scores







class  lowerCAmelCase__   (  lowerCamelCase_     ):






					def __init__(   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											if not isinstance(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		) or min_length < 0:
																	raise ValueError(F'''`min_length` has to be a positive integer, but is {min_length}'''		)

											if not isinstance(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		) or eos_token_id < 0:
																	raise ValueError(F'''`eos_token_id` has to be a positive integer, but is {eos_token_id}'''		)

											lowercase_	:     str													=       min_length
											lowercase_	:     List[Any]													=       eos_token_id






					def __call__(   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_	:     Dict													=       1 - jnp.clip(cur_len - self.min_length				,							0				,							1		)

											lowercase_	:     Dict													=       jnp.where(__SCREAMING_SNAKE_CASE				,							scores.at[:, self.eos_token_id].set(-float('''inf'''		)		)				,							__SCREAMING_SNAKE_CASE		)

											return scores







class  lowerCAmelCase__   (  lowerCamelCase_     ):






					def __init__(   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_	:     Optional[int]													=       list(__SCREAMING_SNAKE_CASE		)
											lowercase_	:     str													=       begin_index






					def __call__(   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_	:     List[str]													=       1 - jnp.bool_(cur_len - self.begin_index		)

											lowercase_	:     Optional[int]													=       jnp.where(__SCREAMING_SNAKE_CASE				,							scores.at[:, self.begin_suppress_tokens].set(-float('''inf'''		)		)				,							__SCREAMING_SNAKE_CASE		)

											return scores







class  lowerCAmelCase__   (  lowerCamelCase_     ):






					def __init__(   self				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_	:     Optional[int]													=       list(__SCREAMING_SNAKE_CASE		)






					def __call__(   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_	:     Tuple													=       scores.at[..., self.suppress_tokens].set(-float('''inf'''		)		)

											return scores







class  lowerCAmelCase__   (  lowerCamelCase_     ):






					def __init__(   self				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_	:     List[str]													=       dict(__SCREAMING_SNAKE_CASE		)
											# Converts the dictionary of format {index: token} containing the tokens to be forced to an array, where the
											# index of the array corresponds to the index of the token to be forced, for XLA compatibility.
											# Indexes without forced tokens will have a negative value.
											lowercase_	:     Optional[int]													=       jnp.ones((max(force_token_map.keys()		) + 1)				,							dtype=jnp.intaa		) * -1
											for index, token in force_token_map.items():
																	if token is not None:
																							lowercase_	:     List[Any]													=       force_token_array.at[index].set(__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Optional[Any]													=       jnp.intaa(__SCREAMING_SNAKE_CASE		)






					def __call__(   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											def _force_token(__SCREAMING_SNAKE_CASE		):
																	lowercase_	:     Any													=       scores.shape[0]
																	lowercase_	:     str													=       self.force_token_array[generation_idx]

																	lowercase_	:     Union[str, Any]													=       jnp.ones_like(__SCREAMING_SNAKE_CASE				,							dtype=scores.dtype		) * -float('''inf'''		)
																	lowercase_	:     Optional[int]													=       jnp.zeros((batch_size, 1)				,							dtype=scores.dtype		)
																	lowercase_	:     int													=       lax.dynamic_update_slice(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							(0, current_token)		)
																	return new_scores

											lowercase_	:     Any													=       lax.cond(
											    cur_len >= self.force_token_array.shape[0]				,							lambda: scores				,							lambda: lax.cond(
											        self.force_token_array[cur_len] >= 0				,							lambda: _force_token(__SCREAMING_SNAKE_CASE		)				,							lambda: scores				,							)				,							)
											return scores







class  lowerCAmelCase__   (  lowerCamelCase_     ):






					def __init__(   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_	:     Optional[int]													=       generate_config.eos_token_id
											lowercase_	:     List[str]													=       generate_config.no_timestamps_token_id
											lowercase_	:     str													=       generate_config.no_timestamps_token_id + 1

											lowercase_	:     Any													=       decoder_input_length + 1

											if generate_config.is_multilingual:
																	# room for language token and task token
																	self.begin_index += 2
											if hasattr(__SCREAMING_SNAKE_CASE				,							'''max_initial_timestamp_index'''		):
																	lowercase_	:     List[Any]													=       generate_config.max_initial_timestamp_index
											else:
																	lowercase_	:     Dict													=       model_config.vocab_size
											if self.max_initial_timestamp_index is None:
																	lowercase_	:     Optional[Any]													=       model_config.vocab_size






					def __call__(   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_	:     Any													=       scores.at[:, self.no_timestamps_token_id].set(-float('''inf'''		)		)

											def handle_pairs(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		):
																	lowercase_	:     int													=       jnp.where((cur_len - self.begin_index) >= 1				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		)
																	lowercase_	:     Dict													=       jnp.where(
																	    input_ids_k[cur_len - 1] >= self.timestamp_begin				,							True and last_was_timestamp				,							__SCREAMING_SNAKE_CASE				,							)

																	lowercase_	:     Optional[int]													=       jnp.where((cur_len - self.begin_index) < 2				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		)
																	lowercase_	:     str													=       jnp.where(
																	    input_ids_k[cur_len - 2] >= self.timestamp_begin				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							)

																	return jnp.where(
																	    __SCREAMING_SNAKE_CASE				,							jnp.where(
																	        penultimate_was_timestamp > 0				,							scores_k.at[self.timestamp_begin :].set(-float('''inf'''		)		)				,							scores_k.at[: self.eos_token_id].set(-float('''inf'''		)		)				,							)				,							__SCREAMING_SNAKE_CASE				,							)

											lowercase_	:     Dict													=       jax.vmap(__SCREAMING_SNAKE_CASE		)(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		)

											lowercase_	:     Union[str, Any]													=       jnp.where(cur_len == self.begin_index				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Optional[Any]													=       jnp.where(
											    self.max_initial_timestamp_index is not None				,							True and apply_max_initial_timestamp				,							__SCREAMING_SNAKE_CASE				,							)

											lowercase_	:     Union[str, Any]													=       self.timestamp_begin + self.max_initial_timestamp_index

											lowercase_	:     Optional[Any]													=       jnp.where(
											    __SCREAMING_SNAKE_CASE				,							scores.at[:, last_allowed + 1 :].set(-float('''inf'''		)		)				,							__SCREAMING_SNAKE_CASE				,							)

											# if sum of probability over timestamps is above any other token, sample timestamp
											lowercase_	:     List[Any]													=       jax.nn.log_softmax(__SCREAMING_SNAKE_CASE				,							axis=-1		)

											def handle_cumulative_probs(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		):
																	lowercase_	:     Union[str, Any]													=       jax.nn.logsumexp(logprobs_k[self.timestamp_begin :]				,							axis=-1		)
																	lowercase_	:     List[str]													=       jnp.max(logprobs_k[: self.timestamp_begin]		)
																	return jnp.where(
																	    timestamp_logprob > max_text_token_logprob				,							scores_k.at[: self.timestamp_begin].set(-float('''inf'''		)		)				,							__SCREAMING_SNAKE_CASE				,							)

											lowercase_	:     int													=       jax.vmap(__SCREAMING_SNAKE_CASE		)(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		)

											return scores








import argparse
from pathlib import Path

import torch
from packaging import version
from torch.onnx import export

from diffusers import AutoencoderKL


SCREAMING_SNAKE_CASE_:List[Any]             =						version.parse(version.parse(torch.__version__).base_version) < version.parse("""1.11""")




def 	__UpperCamelCase	(  _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase=False      ,     )       ->  int:



    """simple docstring"""






    output_path.parent.mkdir(parents=_lowerCAmelCase      ,     exist_ok=_lowerCAmelCase		)
    # PyTorch deprecated the `enable_onnx_checker` and `use_external_data_format` arguments in v1.11,
    # so we check the torch version for backwards compatibility
    if is_torch_less_than_1_11:
        export(
            _lowerCAmelCase      ,     _lowerCAmelCase      ,     f=output_path.as_posix()      ,     input_names=_lowerCAmelCase      ,     output_names=_lowerCAmelCase      ,     dynamic_axes=_lowerCAmelCase      ,     do_constant_folding=_lowerCAmelCase      ,     use_external_data_format=_lowerCAmelCase      ,     enable_onnx_checker=_lowerCAmelCase      ,     opset_version=_lowerCAmelCase      ,     )
    else:
        export(
            _lowerCAmelCase      ,     _lowerCAmelCase      ,     f=output_path.as_posix()      ,     input_names=_lowerCAmelCase      ,     output_names=_lowerCAmelCase      ,     dynamic_axes=_lowerCAmelCase      ,     do_constant_folding=_lowerCAmelCase      ,     opset_version=_lowerCAmelCase      ,     )




@torch.no_grad()
def 	__UpperCamelCase	(  _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase = False		)       ->  List[Any]:



    """simple docstring"""






    A :       Tuple          =   torch.floataa if fpaa else torch.floataa
    if fpaa and torch.cuda.is_available():
        A :       Union[str, Any]          =   """cuda"""
    elif fpaa and not torch.cuda.is_available():
        raise ValueError("""`float16` model export is only supported on GPUs with CUDA"""		)
    else:
        A :       Any          =   """cpu"""
    A :       Any          =   Path(_lowerCAmelCase		)

    # VAE DECODER
    A :       Union[str, Any]          =   AutoencoderKL.from_pretrained(model_path + """/vae"""		)
    A :       Any          =   vae_decoder.config.latent_channels
    # forward only through the decoder part
    A :       Optional[int]          =   vae_decoder.decode
    onnx_export(
        _lowerCAmelCase      ,     model_args=(
            torch.randn(1      ,     _lowerCAmelCase      ,     25      ,     25		).to(device=_lowerCAmelCase      ,     dtype=_lowerCAmelCase		),
            False,
        )      ,     output_path=output_path / """vae_decoder""" / """model.onnx"""      ,     ordered_input_names=["""latent_sample""", """return_dict"""]      ,     output_names=["""sample"""]      ,     dynamic_axes={
            """latent_sample""": {0: """batch""", 1: """channels""", 2: """height""", 3: """width"""},
        }      ,     opset=_lowerCAmelCase      ,     )
    del vae_decoder


if __name__ == "__main__":
     SCREAMING_SNAKE_CASE_:Tuple             =						argparse.ArgumentParser()

     parser.add_argument(
         """--model_path""",
         type=str,
         required=True,
         help="""Path to the `diffusers` checkpoint to convert (either a local directory or on the Hub).""",
     )

     parser.add_argument("""--output_path""", type=str, required=True, help="""Path to the output model.""")
     parser.add_argument(
         """--opset""",
         default=14,
         type=int,
         help="""The version of the ONNX operator set to use.""",
     )
     parser.add_argument("""--fp16""", action="""store_true""", default=False, help="""Export the models in `float16` mode""")

     SCREAMING_SNAKE_CASE_:Tuple             =						parser.parse_args()
     print(args.output_path)
     convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
     print("""SD: Done: ONNX""")


