



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						  =		get_logger(__name__)


_lowercase						  =		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     _UpperCAmelCase			:




     @add_start_docstrings(a__)
     def __call__(				self ,						a__ ,						a__):
         raise NotImplementedError(
             F"{self.__class__} is an abstract class. Only classes inheriting this class can be called.")







class     _UpperCAmelCase			:




     @add_start_docstrings(a__)
     def __call__(				self ,						a__ ,						a__):
         raise NotImplementedError(
             F"{self.__class__} is an abstract class. Only classes inheriting this class can be called.")







class     _UpperCAmelCase			(       A__     ):




     @add_start_docstrings(a__)
     def __call__(				self ,						a__ ,						a__ ,						a__ ,						**a__):
         for processor in self:
             A__		 =					inspect.signature(processor.__call__).parameters
             if len(a__) > 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.")
                 A__		 =					processor(a__ ,						a__ ,						a__ ,						**a__)
             else:
                 A__		 =					processor(a__ ,						a__ ,						a__)
         return scores







class     _UpperCAmelCase			(       A__     ):




     def __init__(				self ,						a__):
         if not isinstance(a__ ,						a__) or not (temperature > 0):
             raise ValueError(F"`temperature` has to be a strictly positive float, but is {temperature}")

         A__		 =					temperature




     def __call__(				self ,						a__ ,						a__ ,						a__):
         A__		 =					scores / self.temperature
         return scores







class     _UpperCAmelCase			(       A__     ):




     def __init__(				self ,						a__ ,						a__ = -float('''Inf''') ,						a__ = 1):
         if not isinstance(a__ ,						a__) 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(a__ ,						a__) 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}")

         A__		 =					top_p
         A__		 =					filter_value
         A__		 =					min_tokens_to_keep




     def __call__(				self ,						a__ ,						a__ ,						a__):
         A__ ,	A__		 =					lax.top_k(a__ ,						scores.shape[-1])

         A__		 =					jnp.full_like(a__ ,						self.filter_value)
         A__		 =					jax.nn.softmax(a__ ,						axis=-1).cumsum(axis=-1)
         A__		 =					cumulative_probs < self.top_p

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

         # min tokens to keep
         A__		 =					score_mask.at[:, : self.min_tokens_to_keep].set(a__)

         A__		 =					jnp.where(a__ ,						a__ ,						a__)
         A__		 =					jax.lax.sort_key_val(a__ ,						a__)[-1]

         return next_scores







class     _UpperCAmelCase			(       A__     ):




     def __init__(				self ,						a__ ,						a__ = -float('''Inf''') ,						a__ = 1):
         if not isinstance(a__ ,						a__) or top_k <= 0:
             raise ValueError(F"`top_k` has to be a strictly positive integer, but is {top_k}")

         A__		 =					max(a__ ,						a__)
         A__		 =					filter_value




     def __call__(				self ,						a__ ,						a__ ,						a__):
         A__ ,	A__		 =					scores.shape
         A__		 =					jnp.full(batch_size * vocab_size ,						self.filter_value)

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

         A__		 =					next_scores_flat.at[topk_indices_flat].set(a__)
         A__		 =					next_scores_flat.reshape(a__ ,						a__)
         return next_scores







class     _UpperCAmelCase			(       A__     ):




     def __init__(				self ,						a__):
         A__		 =					bos_token_id




     def __call__(				self ,						a__ ,						a__ ,						a__):
         A__		 =					jnp.full(scores.shape ,						-float('''inf'''))

         A__		 =					1 - jnp.bool_(cur_len - 1)

         A__		 =					jnp.where(a__ ,						new_scores.at[:, self.bos_token_id].set(0) ,						a__)

         return scores







class     _UpperCAmelCase			(       A__     ):




     def __init__(				self ,						a__ ,						a__):
         A__		 =					max_length
         A__		 =					eos_token_id




     def __call__(				self ,						a__ ,						a__ ,						a__):
         A__		 =					jnp.full(scores.shape ,						-float('''inf'''))

         A__		 =					1 - jnp.bool_(cur_len - self.max_length + 1)

         A__		 =					jnp.where(a__ ,						new_scores.at[:, self.eos_token_id].set(0) ,						a__)

         return scores







class     _UpperCAmelCase			(       A__     ):




     def __init__(				self ,						a__ ,						a__):
         if not isinstance(a__ ,						a__) or min_length < 0:
             raise ValueError(F"`min_length` has to be a positive integer, but is {min_length}")

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

         A__		 =					min_length
         A__		 =					eos_token_id




     def __call__(				self ,						a__ ,						a__ ,						a__):
         # create boolean flag to decide if min length penalty should be applied
         A__		 =					1 - jnp.clip(cur_len - self.min_length ,						0 ,						1)

         A__		 =					jnp.where(a__ ,						scores.at[:, self.eos_token_id].set(-float('''inf''')) ,						a__)

         return scores







class     _UpperCAmelCase			(       A__     ):




     def __init__(				self ,						a__ ,						a__):
         A__		 =					list(a__)
         A__		 =					begin_index




     def __call__(				self ,						a__ ,						a__ ,						a__):
         A__		 =					1 - jnp.bool_(cur_len - self.begin_index)

         A__		 =					jnp.where(a__ ,						scores.at[:, self.begin_suppress_tokens].set(-float('''inf''')) ,						a__)

         return scores







class     _UpperCAmelCase			(       A__     ):




     def __init__(				self ,						a__):
         A__		 =					list(a__)




     def __call__(				self ,						a__ ,						a__ ,						a__):
         A__		 =					scores.at[..., self.suppress_tokens].set(-float('''inf'''))

         return scores







class     _UpperCAmelCase			(       A__     ):




     def __init__(				self ,						a__):
         A__		 =					dict(a__)
         # 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.
         A__		 =					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:
                 A__		 =					force_token_array.at[index].set(a__)
         A__		 =					jnp.intaa(a__)




     def __call__(				self ,						a__ ,						a__ ,						a__):
         def _force_token(a__):
             A__		 =					scores.shape[0]
             A__		 =					self.force_token_array[generation_idx]

             A__		 =					jnp.ones_like(a__ ,						dtype=scores.dtype) * -float('''inf''')
             A__		 =					jnp.zeros((batch_size, 1) ,						dtype=scores.dtype)
             A__		 =					lax.dynamic_update_slice(a__ ,						a__ ,						(0, current_token))
             return new_scores

         A__		 =					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(a__) ,						lambda: scores ,						) ,						)
         return scores







class     _UpperCAmelCase			(       A__     ):




     def __init__(				self ,						a__ ,						a__ ,						a__):
         A__		 =					generate_config.eos_token_id
         A__		 =					generate_config.no_timestamps_token_id
         A__		 =					generate_config.no_timestamps_token_id + 1

         A__		 =					decoder_input_length + 1

         if generate_config.is_multilingual:
             # room for language token and task token
             self.begin_index += 2
         if hasattr(a__ ,						'''max_initial_timestamp_index'''):
             A__		 =					generate_config.max_initial_timestamp_index
         else:
             A__		 =					model_config.vocab_size
         if self.max_initial_timestamp_index is None:
             A__		 =					model_config.vocab_size




     def __call__(				self ,						a__ ,						a__ ,						a__):
         # suppress <|notimestamps|> which is handled by without_timestamps
         A__		 =					scores.at[:, self.no_timestamps_token_id].set(-float('''inf'''))

         def handle_pairs(a__ ,						a__):
             A__		 =					jnp.where((cur_len - self.begin_index) >= 1 ,						a__ ,						a__)
             A__		 =					jnp.where(
                 input_ids_k[cur_len - 1] >= self.timestamp_begin ,						True and last_was_timestamp ,						a__ ,						)

             A__		 =					jnp.where((cur_len - self.begin_index) < 2 ,						a__ ,						a__)
             A__		 =					jnp.where(
                 input_ids_k[cur_len - 2] >= self.timestamp_begin ,						a__ ,						a__ ,						)

             return jnp.where(
                 a__ ,						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''')) ,						) ,						a__ ,						)

         A__		 =					jax.vmap(a__)(a__ ,						a__)

         A__		 =					jnp.where(cur_len == self.begin_index ,						a__ ,						a__)
         A__		 =					jnp.where(
             self.max_initial_timestamp_index is not None ,						True and apply_max_initial_timestamp ,						a__ ,						)

         A__		 =					self.timestamp_begin + self.max_initial_timestamp_index

         A__		 =					jnp.where(
             a__ ,						scores.at[:, last_allowed + 1 :].set(-float('''inf''')) ,						a__ ,						)

         # if sum of probability over timestamps is above any other token, sample timestamp
         A__		 =					jax.nn.log_softmax(a__ ,						axis=-1)

         def handle_cumulative_probs(a__ ,						a__):
             A__		 =					jax.nn.logsumexp(logprobs_k[self.timestamp_begin :] ,						axis=-1)
             A__		 =					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''')) ,						a__ ,						)

         A__		 =					jax.vmap(a__)(a__ ,						a__)

         return scores





import jax.numpy as jnp

from ...utils import logging
from ..ta.modeling_flax_ta import FlaxTaEncoderModel, FlaxTaForConditionalGeneration, FlaxTaModel
from .configuration_mta import MTaConfig


_lowercase						  =		logging.get_logger(__name__)

_lowercase						  =		"T5Config"



def 		lowerCAmelCase__    (					UpperCamelCase_				:       jnp.array ,       UpperCamelCase_				:       int ,       UpperCamelCase_				:       int	)->  jnp.ndarray:
    A__		 =					jnp.zeros_like(UpperCamelCase_	)
    A__		 =					shifted_input_ids.at[:, 1:].set(input_ids[:, :-1]	)
    A__		 =					shifted_input_ids.at[:, 0].set(UpperCamelCase_	)

    A__		 =					jnp.where(shifted_input_ids == -1_0_0 ,       UpperCamelCase_ ,       UpperCamelCase_	)
    return shifted_input_ids



class     _UpperCAmelCase			(       A__     ):
     UpperCamelCase__		    =				'''mt5'''
     UpperCamelCase__		    =				MTaConfig







class     _UpperCAmelCase			(       A__     ):
     UpperCamelCase__		    =				'''mt5'''
     UpperCamelCase__		    =				MTaConfig







class     _UpperCAmelCase			(       A__     ):
     UpperCamelCase__		    =				'''mt5'''
     UpperCamelCase__		    =				MTaConfig
