



"""simple docstring"""






import argparse
import re
from pathlib import Path

import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor

from transformers import (
    EfficientFormerConfig,
    EfficientFormerForImageClassificationWithTeacher,
    EfficientFormerImageProcessor,
)
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling


def      _lowercase							(    __snake_case   ,__snake_case      )		->  Optional[Any]:
			__lowerCAmelCase :    str				=			old_name

			if "patch_embed" in old_name:
						__lowerCAmelCase		,      __lowerCAmelCase		,      __lowerCAmelCase :    Tuple				=			old_name.split("."      )

						if layer == "0":
									__lowerCAmelCase :    str				=			old_name.replace("0"   ,"convolution1"      )
						elif layer == "1":
									__lowerCAmelCase :    List[Any]				=			old_name.replace("1"   ,"batchnorm_before"      )
						elif layer == "3":
									__lowerCAmelCase :    Union[str, Any]				=			old_name.replace("3"   ,"convolution2"      )
						else:
									__lowerCAmelCase :    Optional[int]				=			old_name.replace("4"   ,"batchnorm_after"      )

			if "network" in old_name and re.search(r"\d\.\d"   ,__snake_case      ):
						__lowerCAmelCase :    Optional[Any]				=			r"\b\d{2}\b"
						if bool(re.search(__snake_case   ,__snake_case      )      ):
									__lowerCAmelCase :    Tuple				=			re.search(r"\d\.\d\d."   ,__snake_case      ).group()
						else:
									__lowerCAmelCase :    Optional[Any]				=			re.search(r"\d\.\d."   ,__snake_case      ).group()
						if int(match[0]      ) < 6:
									__lowerCAmelCase :    Union[str, Any]				=			old_name.replace(__snake_case   ,""      )
									__lowerCAmelCase :    List[str]				=			trimmed_name.replace("network"   ,match[0] + ".meta4D_layers.blocks." + match[2:-1]      )
									__lowerCAmelCase :    Optional[Any]				=			"intermediate_stages." + trimmed_name
						else:
									__lowerCAmelCase :    Optional[int]				=			old_name.replace(__snake_case   ,""      )
									if int(match[2]      ) < num_meta4D_last_stage:
												__lowerCAmelCase :    Any				=			trimmed_name.replace("network"   ,"meta4D_layers.blocks." + match[2]      )
									else:
												__lowerCAmelCase :    str				=			str(int(match[2]      ) - num_meta4D_last_stage      )
												__lowerCAmelCase :    List[str]				=			trimmed_name.replace("network"   ,"meta3D_layers.blocks." + layer_index      )
												if "norm1" in old_name:
															__lowerCAmelCase :    Union[str, Any]				=			trimmed_name.replace("norm1"   ,"layernorm1"      )
												elif "norm2" in old_name:
															__lowerCAmelCase :    Any				=			trimmed_name.replace("norm2"   ,"layernorm2"      )
												elif "fc1" in old_name:
															__lowerCAmelCase :    Optional[int]				=			trimmed_name.replace("fc1"   ,"linear_in"      )
												elif "fc2" in old_name:
															__lowerCAmelCase :    Tuple				=			trimmed_name.replace("fc2"   ,"linear_out"      )

									__lowerCAmelCase :    Dict				=			"last_stage." + trimmed_name

			elif "network" in old_name and re.search(r".\d."   ,__snake_case      ):
						__lowerCAmelCase :    Dict				=			old_name.replace("network"   ,"intermediate_stages"      )

			if "fc" in new_name:
						__lowerCAmelCase :    Tuple				=			new_name.replace("fc"   ,"convolution"      )
			elif ("norm1" in new_name) and ("layernorm1" not in new_name):
						__lowerCAmelCase :    Union[str, Any]				=			new_name.replace("norm1"   ,"batchnorm_before"      )
			elif ("norm2" in new_name) and ("layernorm2" not in new_name):
						__lowerCAmelCase :    Optional[int]				=			new_name.replace("norm2"   ,"batchnorm_after"      )
			if "proj" in new_name:
						__lowerCAmelCase :    List[Any]				=			new_name.replace("proj"   ,"projection"      )
			if "dist_head" in new_name:
						__lowerCAmelCase :    Union[str, Any]				=			new_name.replace("dist_head"   ,"distillation_classifier"      )
			elif "head" in new_name:
						__lowerCAmelCase :    Optional[Any]				=			new_name.replace("head"   ,"classifier"      )
			elif "patch_embed" in new_name:
						__lowerCAmelCase :    List[Any]				=			"efficientformer." + new_name
			elif new_name == "norm.weight" or new_name == "norm.bias":
						__lowerCAmelCase :    List[str]				=			new_name.replace("norm"   ,"layernorm"      )
						__lowerCAmelCase :    Union[str, Any]				=			"efficientformer." + new_name
			else:
						__lowerCAmelCase :    List[Any]				=			"efficientformer.encoder." + new_name

			return new_name


def      _lowercase							(    __snake_case   ,__snake_case      )		->  List[str]:
			for key in checkpoint.copy().keys():
						__lowerCAmelCase :    str				=			checkpoint.pop(__snake_case      )
						__lowerCAmelCase :    Optional[int]				=			val

			return checkpoint


def      _lowercase							(    )		->  Any:
			__lowerCAmelCase :    Tuple				=			"http://images.cocodataset.org/val2017/000000039769.jpg"
			__lowerCAmelCase :    Any				=			Image.open(requests.get(__snake_case   ,stream=__snake_case      ).raw      )

			return image


def      _lowercase							(    __snake_case   ,__snake_case   ,__snake_case   ,__snake_case      )		->  Any:
			__lowerCAmelCase :    int				=			torch.load(__snake_case   ,map_location="cpu"      )["model"]
			__lowerCAmelCase :    Union[str, Any]				=			EfficientFormerConfig.from_json_file(__snake_case      )
			__lowerCAmelCase :    List[str]				=			EfficientFormerForImageClassificationWithTeacher(__snake_case      )
			__lowerCAmelCase :    Tuple				=			"_".join(checkpoint_path.split("/"      )[-1].split("."      )[0].split("_"      )[:-1]      )

			__lowerCAmelCase :    Any				=			config.depths[-1] - config.num_metaad_blocks + 1
			__lowerCAmelCase :    Dict				=			convert_torch_checkpoint(__snake_case   ,__snake_case      )

			model.load_state_dict(__snake_case      )
			model.eval()

			__lowerCAmelCase :    Tuple				=			{
			    "bilinear": PILImageResampling.BILINEAR,
			    "bicubic": PILImageResampling.BICUBIC,
			    "nearest": PILImageResampling.NEAREST,
			}

			# prepare image
			__lowerCAmelCase :    Dict				=			prepare_img()
			__lowerCAmelCase :    str				=			256
			__lowerCAmelCase :    List[Any]				=			224
			__lowerCAmelCase :    int				=			EfficientFormerImageProcessor(
			    size={"shortest_edge": image_size}   ,crop_size={"height": crop_size, "width": crop_size}   ,resample=pillow_resamplings["bicubic"]   ,)
			__lowerCAmelCase :    Dict				=			processor(images=__snake_case   ,return_tensors="pt"      ).pixel_values

			# original processing pipeline
			__lowerCAmelCase :    Optional[Any]				=			Compose(
			    [
			        Resize(__snake_case   ,interpolation=pillow_resamplings["bicubic"]      ),
			        CenterCrop(__snake_case      ),
			        ToTensor(),
			        Normalize(__snake_case   ,__snake_case      ),
			    ]      )
			__lowerCAmelCase :    Any				=			image_transforms(__snake_case      ).unsqueeze(0      )

			assert torch.allclose(__snake_case   ,__snake_case      )

			__lowerCAmelCase :    Union[str, Any]				=			model(__snake_case      )
			__lowerCAmelCase :    Any				=			outputs.logits

			__lowerCAmelCase :    Tuple				=			(1, 1_000)

			if "l1" in model_name:
						__lowerCAmelCase :    List[Any]				=			torch.Tensor(
						    [-0.1312, 0.4353, -1.0499, -0.5124, 0.4183, -0.6793, -1.3777, -0.0893, -0.7358, -2.4328]      )
						assert torch.allclose(logits[0, :10]   ,__snake_case   ,atol=1e-3      )
						assert logits.shape == expected_shape
			elif "l3" in model_name:
						__lowerCAmelCase :    Union[str, Any]				=			torch.Tensor(
						    [-1.3150, -1.5456, -1.2556, -0.8496, -0.7127, -0.7897, -0.9728, -0.3052, 0.3751, -0.3127]      )
						assert torch.allclose(logits[0, :10]   ,__snake_case   ,atol=1e-3      )
						assert logits.shape == expected_shape
			elif "l7" in model_name:
						__lowerCAmelCase :    Union[str, Any]				=			torch.Tensor(
						    [-1.0283, -1.4131, -0.5644, -1.3115, -0.5785, -1.2049, -0.7528, 0.1992, -0.3822, -0.0878]      )
						assert logits.shape == expected_shape
			else:
						raise ValueError(
						    F"""Unknown model checkpoint: {checkpoint_path}. Supported version of efficientformer are l1, l3 and l7"""      )

			# Save Checkpoints
			Path(__snake_case      ).mkdir(exist_ok=__snake_case      )
			model.save_pretrained(__snake_case      )
			print(F"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"""      )
			processor.save_pretrained(__snake_case      )
			print(F"""Processor successfuly saved at {pytorch_dump_path}"""      )

			if push_to_hub:
						print("Pushing model to the hub..."      )

						model.push_to_hub(
						    repo_id=F"""Bearnardd/{pytorch_dump_path}"""   ,commit_message="Add model"   ,use_temp_dir=__snake_case   ,)
						processor.push_to_hub(
						    repo_id=F"""Bearnardd/{pytorch_dump_path}"""   ,commit_message="Add image processor"   ,use_temp_dir=__snake_case   ,)


if __name__ == "__main__":
	__snake_case				:  str =       argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '--pytorch_model_path',
	    default=None,
	    type=str,
	    required=True,
	    help='Path to EfficientFormer pytorch checkpoint.',
	)
	parser.add_argument(
	    '--config_file',
	    default=None,
	    type=str,
	    required=True,
	    help='The json file for EfficientFormer model config.',
	)
	parser.add_argument(
	    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
	)

	parser.add_argument('--push_to_hub', action='store_true', help='Push model and image processor to the hub')
	parser.add_argument(
	    '--no-push_to_hub',
	    dest='push_to_hub',
	    action='store_false',
	    help='Do not push model and image processor to the hub',
	)
	parser.set_defaults(push_to_hub=True)

	__snake_case				:  str =       parser.parse_args()
	convert_efficientformer_checkpoint(
	    checkpoint_path=args.pytorch_model_path,
	    efficientformer_config_file=args.config_file,
	    pytorch_dump_path=args.pytorch_dump_path,
	    push_to_hub=args.push_to_hub,
	)




"""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


__snake_case				:  List[str] =       get_logger(__name__)


__snake_case				:  str =       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      A__  :




	'''simple docstring'''





	@add_start_docstrings(_SCREAMING_SNAKE_CASE)
	def __call__(	self:						Tuple			,     _SCREAMING_SNAKE_CASE:						jnp.ndarray			,     _SCREAMING_SNAKE_CASE:						jnp.ndarray)							->			jnp.ndarray:

				"""simple docstring"""

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






class      A__  :




	'''simple docstring'''





	@add_start_docstrings(_SCREAMING_SNAKE_CASE)
	def __call__(	self:						List[str]			,     _SCREAMING_SNAKE_CASE:						jnp.ndarray			,     _SCREAMING_SNAKE_CASE:						jnp.ndarray)							->			jnp.ndarray:

				"""simple docstring"""

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






class      A__  (    __SCREAMING_SNAKE_CASE				):




	'''simple docstring'''





	@add_start_docstrings(_SCREAMING_SNAKE_CASE)
	def __call__(	self:						Optional[int]			,     _SCREAMING_SNAKE_CASE:						jnp.ndarray			,     _SCREAMING_SNAKE_CASE:						jnp.ndarray			,     _SCREAMING_SNAKE_CASE:						int			,     **_SCREAMING_SNAKE_CASE:						str)							->			jnp.ndarray:

				"""simple docstring"""

				for processor in self:
							__lowerCAmelCase :    int				=			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.""")
										__lowerCAmelCase :    int				=			processor(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE			,     **_SCREAMING_SNAKE_CASE)
							else:
										__lowerCAmelCase :    Union[str, Any]				=			processor(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE)
				return scores






class      A__  (    __SCREAMING_SNAKE_CASE				):




	'''simple docstring'''





	def __init__(	self:						int			,     _SCREAMING_SNAKE_CASE:						float)							->			str:

				"""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}""")

				__lowerCAmelCase :    List[str]				=			temperature







	def __call__(	self:						Dict			,     _SCREAMING_SNAKE_CASE:						jnp.ndarray			,     _SCREAMING_SNAKE_CASE:						jnp.ndarray			,     _SCREAMING_SNAKE_CASE:						int)							->			jnp.ndarray:

				"""simple docstring"""

				__lowerCAmelCase :    Union[str, Any]				=			scores / self.temperature
				return scores






class      A__  (    __SCREAMING_SNAKE_CASE				):




	'''simple docstring'''





	def __init__(	self:						Dict			,     _SCREAMING_SNAKE_CASE:						float			,     _SCREAMING_SNAKE_CASE:						float = -float("Inf")			,     _SCREAMING_SNAKE_CASE:						int = 1)							->			Optional[int]:

				"""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}""")

				__lowerCAmelCase :    Union[str, Any]				=			top_p
				__lowerCAmelCase :    Optional[int]				=			filter_value
				__lowerCAmelCase :    str				=			min_tokens_to_keep







	def __call__(	self:						Union[str, Any]			,     _SCREAMING_SNAKE_CASE:						jnp.ndarray			,     _SCREAMING_SNAKE_CASE:						jnp.ndarray			,     _SCREAMING_SNAKE_CASE:						int)							->			jnp.ndarray:

				"""simple docstring"""

				__lowerCAmelCase		,      __lowerCAmelCase :    Optional[Any]				=			lax.top_k(_SCREAMING_SNAKE_CASE			,     scores.shape[-1])

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

				# include the token that is higher than top_p as well
				__lowerCAmelCase :    Tuple				=			jnp.roll(_SCREAMING_SNAKE_CASE			,     1)
				score_mask |= score_mask.at[:, 0].set(_SCREAMING_SNAKE_CASE)

				# min tokens to keep
				__lowerCAmelCase :    Union[str, Any]				=			score_mask.at[:, : self.min_tokens_to_keep].set(_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase :    Optional[Any]				=			jnp.where(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE)
				__lowerCAmelCase :    Dict				=			jax.lax.sort_key_val(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE)[-1]

				return next_scores






class      A__  (    __SCREAMING_SNAKE_CASE				):




	'''simple docstring'''





	def __init__(	self:						Tuple			,     _SCREAMING_SNAKE_CASE:						int			,     _SCREAMING_SNAKE_CASE:						float = -float("Inf")			,     _SCREAMING_SNAKE_CASE:						int = 1)							->			Optional[int]:

				"""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}""")

				__lowerCAmelCase :    Any				=			max(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE)
				__lowerCAmelCase :    Optional[int]				=			filter_value







	def __call__(	self:						List[Any]			,     _SCREAMING_SNAKE_CASE:						jnp.ndarray			,     _SCREAMING_SNAKE_CASE:						jnp.ndarray			,     _SCREAMING_SNAKE_CASE:						int)							->			jnp.ndarray:

				"""simple docstring"""

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

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

				__lowerCAmelCase :    str				=			next_scores_flat.at[topk_indices_flat].set(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase :    Union[str, Any]				=			next_scores_flat.reshape(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE)
				return next_scores






class      A__  (    __SCREAMING_SNAKE_CASE				):




	'''simple docstring'''





	def __init__(	self:						List[str]			,     _SCREAMING_SNAKE_CASE:						int)							->			Optional[int]:

				"""simple docstring"""

				__lowerCAmelCase :    Tuple				=			bos_token_id







	def __call__(	self:						Optional[int]			,     _SCREAMING_SNAKE_CASE:						jnp.ndarray			,     _SCREAMING_SNAKE_CASE:						jnp.ndarray			,     _SCREAMING_SNAKE_CASE:						int)							->			jnp.ndarray:

				"""simple docstring"""

				__lowerCAmelCase :    int				=			jnp.full(scores.shape			,     -float("inf"))

				__lowerCAmelCase :    int				=			1 - jnp.bool_(cur_len - 1)

				__lowerCAmelCase :    Tuple				=			jnp.where(_SCREAMING_SNAKE_CASE			,     new_scores.at[:, self.bos_token_id].set(0)			,     _SCREAMING_SNAKE_CASE)

				return scores






class      A__  (    __SCREAMING_SNAKE_CASE				):




	'''simple docstring'''





	def __init__(	self:						List[Any]			,     _SCREAMING_SNAKE_CASE:						int			,     _SCREAMING_SNAKE_CASE:						int)							->			int:

				"""simple docstring"""

				__lowerCAmelCase :    List[str]				=			max_length
				__lowerCAmelCase :    int				=			eos_token_id







	def __call__(	self:						List[Any]			,     _SCREAMING_SNAKE_CASE:						jnp.ndarray			,     _SCREAMING_SNAKE_CASE:						jnp.ndarray			,     _SCREAMING_SNAKE_CASE:						int)							->			jnp.ndarray:

				"""simple docstring"""

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

				__lowerCAmelCase :    Any				=			1 - jnp.bool_(cur_len - self.max_length + 1)

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

				return scores






class      A__  (    __SCREAMING_SNAKE_CASE				):




	'''simple docstring'''





	def __init__(	self:						Optional[Any]			,     _SCREAMING_SNAKE_CASE:						int			,     _SCREAMING_SNAKE_CASE:						int)							->			Optional[Any]:

				"""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}""")

				__lowerCAmelCase :    Any				=			min_length
				__lowerCAmelCase :    Tuple				=			eos_token_id







	def __call__(	self:						str			,     _SCREAMING_SNAKE_CASE:						jnp.ndarray			,     _SCREAMING_SNAKE_CASE:						jnp.ndarray			,     _SCREAMING_SNAKE_CASE:						int)							->			jnp.ndarray:

				"""simple docstring"""

				__lowerCAmelCase :    Optional[Any]				=			1 - jnp.clip(cur_len - self.min_length			,     0			,     1)

				__lowerCAmelCase :    Union[str, Any]				=			jnp.where(_SCREAMING_SNAKE_CASE			,     scores.at[:, self.eos_token_id].set(-float("inf"))			,     _SCREAMING_SNAKE_CASE)

				return scores






class      A__  (    __SCREAMING_SNAKE_CASE				):




	'''simple docstring'''





	def __init__(	self:						Optional[Any]			,     _SCREAMING_SNAKE_CASE:						Optional[Any]			,     _SCREAMING_SNAKE_CASE:						str)							->			List[Any]:

				"""simple docstring"""

				__lowerCAmelCase :    int				=			list(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase :    Optional[Any]				=			begin_index







	def __call__(	self:						Optional[Any]			,     _SCREAMING_SNAKE_CASE:						List[str]			,     _SCREAMING_SNAKE_CASE:						str			,     _SCREAMING_SNAKE_CASE:						int)							->			Dict:

				"""simple docstring"""

				__lowerCAmelCase :    Any				=			1 - jnp.bool_(cur_len - self.begin_index)

				__lowerCAmelCase :    Optional[Any]				=			jnp.where(_SCREAMING_SNAKE_CASE			,     scores.at[:, self.begin_suppress_tokens].set(-float("inf"))			,     _SCREAMING_SNAKE_CASE)

				return scores






class      A__  (    __SCREAMING_SNAKE_CASE				):




	'''simple docstring'''





	def __init__(	self:						List[Any]			,     _SCREAMING_SNAKE_CASE:						list)							->			Optional[int]:

				"""simple docstring"""

				__lowerCAmelCase :    Optional[Any]				=			list(_SCREAMING_SNAKE_CASE)







	def __call__(	self:						int			,     _SCREAMING_SNAKE_CASE:						jnp.ndarray			,     _SCREAMING_SNAKE_CASE:						jnp.ndarray			,     _SCREAMING_SNAKE_CASE:						int)							->			jnp.ndarray:

				"""simple docstring"""

				__lowerCAmelCase :    Optional[Any]				=			scores.at[..., self.suppress_tokens].set(-float("inf"))

				return scores






class      A__  (    __SCREAMING_SNAKE_CASE				):




	'''simple docstring'''





	def __init__(	self:						List[str]			,     _SCREAMING_SNAKE_CASE:						int)							->			str:

				"""simple docstring"""

				__lowerCAmelCase :    Optional[Any]				=			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.
				__lowerCAmelCase :    Any				=			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:
										__lowerCAmelCase :    int				=			force_token_array.at[index].set(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase :    Tuple				=			jnp.intaa(_SCREAMING_SNAKE_CASE)







	def __call__(	self:						List[str]			,     _SCREAMING_SNAKE_CASE:						jnp.ndarray			,     _SCREAMING_SNAKE_CASE:						jnp.ndarray			,     _SCREAMING_SNAKE_CASE:						int)							->			jnp.ndarray:

				"""simple docstring"""

				def _force_token(_SCREAMING_SNAKE_CASE:						Optional[Any]):
							__lowerCAmelCase :    int				=			scores.shape[0]
							__lowerCAmelCase :    Optional[int]				=			self.force_token_array[generation_idx]

							__lowerCAmelCase :    int				=			jnp.ones_like(_SCREAMING_SNAKE_CASE			,     dtype=scores.dtype) * -float("inf")
							__lowerCAmelCase :    Optional[int]				=			jnp.zeros((batch_size, 1)			,     dtype=scores.dtype)
							__lowerCAmelCase :    Dict				=			lax.dynamic_update_slice(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE			,     (0, current_token))
							return new_scores

				__lowerCAmelCase :    Union[str, 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      A__  (    __SCREAMING_SNAKE_CASE				):




	'''simple docstring'''





	def __init__(	self:						Optional[Any]			,     _SCREAMING_SNAKE_CASE:						Optional[Any]			,     _SCREAMING_SNAKE_CASE:						Tuple			,     _SCREAMING_SNAKE_CASE:						Optional[Any])							->			int:

				"""simple docstring"""

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

				__lowerCAmelCase :    Optional[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"):
							__lowerCAmelCase :    Optional[int]				=			generate_config.max_initial_timestamp_index
				else:
							__lowerCAmelCase :    Optional[int]				=			model_config.vocab_size
				if self.max_initial_timestamp_index is None:
							__lowerCAmelCase :    int				=			model_config.vocab_size







	def __call__(	self:						Tuple			,     _SCREAMING_SNAKE_CASE:						Union[str, Any]			,     _SCREAMING_SNAKE_CASE:						Optional[Any]			,     _SCREAMING_SNAKE_CASE:						int)							->			Optional[int]:

				"""simple docstring"""

				__lowerCAmelCase :    Optional[Any]				=			scores.at[:, self.no_timestamps_token_id].set(-float("inf"))

				def handle_pairs(_SCREAMING_SNAKE_CASE:						int			,     _SCREAMING_SNAKE_CASE:						Union[str, Any]):
							__lowerCAmelCase :    Tuple				=			jnp.where((cur_len - self.begin_index) >= 1			,     _SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE)
							__lowerCAmelCase :    List[Any]				=			jnp.where(
							    input_ids_k[cur_len - 1] >= self.timestamp_begin			,     True and last_was_timestamp			,     _SCREAMING_SNAKE_CASE			,     )

							__lowerCAmelCase :    Tuple				=			jnp.where((cur_len - self.begin_index) < 2			,     _SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE)
							__lowerCAmelCase :    int				=			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			,     )

				__lowerCAmelCase :    int				=			jax.vmap(_SCREAMING_SNAKE_CASE)(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE)

				__lowerCAmelCase :    List[str]				=			jnp.where(cur_len == self.begin_index			,     _SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE)
				__lowerCAmelCase :    Any				=			jnp.where(
				    self.max_initial_timestamp_index is not None			,     True and apply_max_initial_timestamp			,     _SCREAMING_SNAKE_CASE			,     )

				__lowerCAmelCase :    List[Any]				=			self.timestamp_begin + self.max_initial_timestamp_index

				__lowerCAmelCase :    Tuple				=			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
				__lowerCAmelCase :    List[str]				=			jax.nn.log_softmax(_SCREAMING_SNAKE_CASE			,     axis=-1)

				def handle_cumulative_probs(_SCREAMING_SNAKE_CASE:						Any			,     _SCREAMING_SNAKE_CASE:						Any):
							__lowerCAmelCase :    Tuple				=			jax.nn.logsumexp(logprobs_k[self.timestamp_begin :]			,     axis=-1)
							__lowerCAmelCase :    int				=			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			,     )

				__lowerCAmelCase :    Dict				=			jax.vmap(_SCREAMING_SNAKE_CASE)(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE)

				return scores