
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


_A     =  get_logger(__name__)


_A     =  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(_UpperCamelCase  )
    def __call__(      self       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   jnp.ndarray:
       raise NotImplementedError(
           f"""{self.__class__} is an abstract class. Only classes inheriting this class can be called."""  )





class 			_lowerCAmelCase      :




    @add_start_docstrings(_UpperCamelCase  )
    def __call__(      self       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   jnp.ndarray:
       raise NotImplementedError(
           f"""{self.__class__} is an abstract class. Only classes inheriting this class can be called."""  )





class 			_lowerCAmelCase      (			__a ):




    @add_start_docstrings(_UpperCamelCase  )
    def __call__(      self       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       **_UpperCamelCase  )       ->   jnp.ndarray:
       for processor in self:
          lowerCAmelCase_      			=			inspect.signature(processor.__call__  ).parameters
          if len(_UpperCamelCase  ) > 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_      			=			processor(_UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       **_UpperCamelCase  )
          else:
             lowerCAmelCase_      			=			processor(_UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase  )
       return scores





class 			_lowerCAmelCase      (			__a ):




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

       lowerCAmelCase_      			=			temperature




    def __call__(      self       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   jnp.ndarray:
       lowerCAmelCase_      			=			scores / self.temperature
       return scores





class 			_lowerCAmelCase      (			__a ):




    def __init__(      self       ,       _UpperCamelCase       ,       _UpperCamelCase = -float("Inf"  )       ,       _UpperCamelCase = 1  )       ->   Union[str, Any]:
       if not isinstance(_UpperCamelCase       ,       _UpperCamelCase  ) 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(_UpperCamelCase       ,       _UpperCamelCase  ) 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_      			=			top_p
       lowerCAmelCase_      			=			filter_value
       lowerCAmelCase_      			=			min_tokens_to_keep




    def __call__(      self       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   jnp.ndarray:
       lowerCAmelCase_			,						lowerCAmelCase_      			=			lax.top_k(_UpperCamelCase       ,       scores.shape[-1]  )

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

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

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

       lowerCAmelCase_      			=			jnp.where(_UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase  )
       lowerCAmelCase_      			=			jax.lax.sort_key_val(_UpperCamelCase       ,       _UpperCamelCase  )[-1]

       return next_scores





class 			_lowerCAmelCase      (			__a ):




    def __init__(      self       ,       _UpperCamelCase       ,       _UpperCamelCase = -float("Inf"  )       ,       _UpperCamelCase = 1  )       ->   List[Any]:
       if not isinstance(_UpperCamelCase       ,       _UpperCamelCase  ) or top_k <= 0:
          raise ValueError(f"""`top_k` has to be a strictly positive integer, but is {top_k}"""  )

       lowerCAmelCase_      			=			max(_UpperCamelCase       ,       _UpperCamelCase  )
       lowerCAmelCase_      			=			filter_value




    def __call__(      self       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   jnp.ndarray:
       lowerCAmelCase_			,						lowerCAmelCase_      			=			scores.shape
       lowerCAmelCase_      			=			jnp.full(batch_size * vocab_size       ,       self.filter_value  )

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

       lowerCAmelCase_      			=			next_scores_flat.at[topk_indices_flat].set(_UpperCamelCase  )
       lowerCAmelCase_      			=			next_scores_flat.reshape(_UpperCamelCase       ,       _UpperCamelCase  )
       return next_scores





class 			_lowerCAmelCase      (			__a ):




    def __init__(      self       ,       _UpperCamelCase  )       ->   Any:
       lowerCAmelCase_      			=			bos_token_id




    def __call__(      self       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   jnp.ndarray:
       lowerCAmelCase_      			=			jnp.full(scores.shape       ,       -float("inf"  )  )

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

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

       return scores





class 			_lowerCAmelCase      (			__a ):




    def __init__(      self       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   List[str]:
       lowerCAmelCase_      			=			max_length
       lowerCAmelCase_      			=			eos_token_id




    def __call__(      self       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   jnp.ndarray:
       lowerCAmelCase_      			=			jnp.full(scores.shape       ,       -float("inf"  )  )

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

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

       return scores





class 			_lowerCAmelCase      (			__a ):




    def __init__(      self       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   Optional[int]:
       if not isinstance(_UpperCamelCase       ,       _UpperCamelCase  ) or min_length < 0:
          raise ValueError(f"""`min_length` has to be a positive integer, but is {min_length}"""  )

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

       lowerCAmelCase_      			=			min_length
       lowerCAmelCase_      			=			eos_token_id




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

       lowerCAmelCase_      			=			jnp.where(_UpperCamelCase       ,       scores.at[:, self.eos_token_id].set(-float("inf"  )  )       ,       _UpperCamelCase  )

       return scores





class 			_lowerCAmelCase      (			__a ):




    def __init__(      self       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   int:
       lowerCAmelCase_      			=			list(_UpperCamelCase  )
       lowerCAmelCase_      			=			begin_index




    def __call__(      self       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   int:
       lowerCAmelCase_      			=			1 - jnp.bool_(cur_len - self.begin_index  )

       lowerCAmelCase_      			=			jnp.where(_UpperCamelCase       ,       scores.at[:, self.begin_suppress_tokens].set(-float("inf"  )  )       ,       _UpperCamelCase  )

       return scores





class 			_lowerCAmelCase      (			__a ):




    def __init__(      self       ,       _UpperCamelCase  )       ->   Optional[Any]:
       lowerCAmelCase_      			=			list(_UpperCamelCase  )




    def __call__(      self       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   jnp.ndarray:
       lowerCAmelCase_      			=			scores.at[..., self.suppress_tokens].set(-float("inf"  )  )

       return scores





class 			_lowerCAmelCase      (			__a ):




    def __init__(      self       ,       _UpperCamelCase  )       ->   List[Any]:
       lowerCAmelCase_      			=			dict(_UpperCamelCase  )
       # 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_      			=			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_      			=			force_token_array.at[index].set(_UpperCamelCase  )
       lowerCAmelCase_      			=			jnp.intaa(_UpperCamelCase  )




    def __call__(      self       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   jnp.ndarray:
       def _force_token(_UpperCamelCase  ):
          lowerCAmelCase_      			=			scores.shape[0]
          lowerCAmelCase_      			=			self.force_token_array[generation_idx]

          lowerCAmelCase_      			=			jnp.ones_like(_UpperCamelCase       ,       dtype=scores.dtype  ) * -float("inf"  )
          lowerCAmelCase_      			=			jnp.zeros((batch_size, 1)       ,       dtype=scores.dtype  )
          lowerCAmelCase_      			=			lax.dynamic_update_slice(_UpperCamelCase       ,       _UpperCamelCase       ,       (0, current_token)  )
          return new_scores

       lowerCAmelCase_      			=			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(_UpperCamelCase  )       ,       lambda: scores       ,       )       ,       )
       return scores





class 			_lowerCAmelCase      (			__a ):




    def __init__(      self       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   Tuple:
       lowerCAmelCase_      			=			generate_config.eos_token_id
       lowerCAmelCase_      			=			generate_config.no_timestamps_token_id
       lowerCAmelCase_      			=			generate_config.no_timestamps_token_id + 1

       lowerCAmelCase_      			=			decoder_input_length + 1

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




    def __call__(      self       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   Optional[Any]:
       # suppress <|notimestamps|> which is handled by without_timestamps
       lowerCAmelCase_      			=			scores.at[:, self.no_timestamps_token_id].set(-float("inf"  )  )

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

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

          return jnp.where(
              _UpperCamelCase       ,       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"  )  )       ,       )       ,       _UpperCamelCase       ,       )

       lowerCAmelCase_      			=			jax.vmap(_UpperCamelCase  )(_UpperCamelCase       ,       _UpperCamelCase  )

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

       lowerCAmelCase_      			=			self.timestamp_begin + self.max_initial_timestamp_index

       lowerCAmelCase_      			=			jnp.where(
           _UpperCamelCase       ,       scores.at[:, last_allowed + 1 :].set(-float("inf"  )  )       ,       _UpperCamelCase       ,       )

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

       def handle_cumulative_probs(_UpperCamelCase       ,       _UpperCamelCase  ):
          lowerCAmelCase_      			=			jax.nn.logsumexp(logprobs_k[self.timestamp_begin :]       ,       axis=-1  )
          lowerCAmelCase_      			=			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"  )  )       ,       _UpperCamelCase       ,       )

       lowerCAmelCase_      			=			jax.vmap(_UpperCamelCase  )(_UpperCamelCase       ,       _UpperCamelCase  )

       return scores





import itertools
import string
from collections.abc import Generator, Iterable






def      lowerCamelCase__       (       __lowerCAmelCase						:						Iterable[str]					,							__lowerCAmelCase						:						int       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			iter(__lowerCAmelCase       )
			while True:
						lowerCAmelCase_      			=			tuple(itertools.islice(__lowerCAmelCase					,							__lowerCAmelCase       )       )
						if not chunk:
									return
						yield chunk






def      lowerCamelCase__       (       __lowerCAmelCase						:						str       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			"".join([c.upper() for c in dirty if c in string.ascii_letters]       )
			lowerCAmelCase_      			=			""

			if len(__lowerCAmelCase       ) < 2:
						return dirty

			for i in range(len(__lowerCAmelCase       ) - 1       ):
						clean += dirty[i]

						if dirty[i] == dirty[i + 1]:
									clean += "X"

			clean += dirty[-1]

			if len(__lowerCAmelCase       ) & 1:
						clean += "X"

			return clean






def      lowerCamelCase__       (       __lowerCAmelCase						:						str       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			"ABCDEFGHIKLMNOPQRSTUVWXYZ"
			# we're using a list instead of a '2d' array because it makes the math
			# for setting up the table and doing the actual encoding/decoding simpler
			lowerCAmelCase_      			=			[]

			# copy key chars into the table if they are in `alphabet` ignoring duplicates
			for char in key.upper():
						if char not in table and char in alphabet:
									table.append(__lowerCAmelCase       )

    # fill the rest of the table in with the remaining alphabet chars
			for char in alphabet:
						if char not in table:
									table.append(__lowerCAmelCase       )

			return table






def      lowerCamelCase__       (       __lowerCAmelCase						:						str					,							__lowerCAmelCase						:						str       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			generate_table(__lowerCAmelCase       )
			lowerCAmelCase_      			=			prepare_input(__lowerCAmelCase       )
			lowerCAmelCase_      			=			""

			# https://en.wikipedia.org/wiki/Playfair_cipher#Description
			for chara, chara in chunker(__lowerCAmelCase					,							2       ):
						lowerCAmelCase_			,						lowerCAmelCase_      			=			divmod(table.index(__lowerCAmelCase       )					,							5       )
						lowerCAmelCase_			,						lowerCAmelCase_      			=			divmod(table.index(__lowerCAmelCase       )					,							5       )

						if rowa == rowa:
									ciphertext += table[rowa * 5 + (cola + 1) % 5]
									ciphertext += table[rowa * 5 + (cola + 1) % 5]
						elif cola == cola:
									ciphertext += table[((rowa + 1) % 5) * 5 + cola]
									ciphertext += table[((rowa + 1) % 5) * 5 + cola]
						else:  # rectangle
									ciphertext += table[rowa * 5 + cola]
									ciphertext += table[rowa * 5 + cola]

			return ciphertext






def      lowerCamelCase__       (       __lowerCAmelCase						:						str					,							__lowerCAmelCase						:						str       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			generate_table(__lowerCAmelCase       )
			lowerCAmelCase_      			=			""

			# https://en.wikipedia.org/wiki/Playfair_cipher#Description
			for chara, chara in chunker(__lowerCAmelCase					,							2       ):
						lowerCAmelCase_			,						lowerCAmelCase_      			=			divmod(table.index(__lowerCAmelCase       )					,							5       )
						lowerCAmelCase_			,						lowerCAmelCase_      			=			divmod(table.index(__lowerCAmelCase       )					,							5       )

						if rowa == rowa:
									plaintext += table[rowa * 5 + (cola - 1) % 5]
									plaintext += table[rowa * 5 + (cola - 1) % 5]
						elif cola == cola:
									plaintext += table[((rowa - 1) % 5) * 5 + cola]
									plaintext += table[((rowa - 1) % 5) * 5 + cola]
						else:  # rectangle
									plaintext += table[rowa * 5 + cola]
									plaintext += table[rowa * 5 + cola]

			return plaintext



