
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  lowercase__:



	"""simple docstring"""




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

class  lowercase__:



	"""simple docstring"""




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

class  lowercase__(				UpperCAmelCase							):



	"""simple docstring"""




	@add_start_docstrings(SCREAMING_SNAKE_CASE_       )
	def __call__(       self  :			Optional[Any]       ,							SCREAMING_SNAKE_CASE_  :			jnp.ndarray       ,							SCREAMING_SNAKE_CASE_  :			jnp.ndarray       ,							SCREAMING_SNAKE_CASE_  :			int       ,							**SCREAMING_SNAKE_CASE_  :			Any       )       ->     jnp.ndarray:
					for processor in self:
									lowercase_       =					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_       =					processor(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							**SCREAMING_SNAKE_CASE_       )
									else:
													lowercase_       =					processor(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       )
					return scores

class  lowercase__(				UpperCAmelCase							):



	"""simple docstring"""




	def __init__(       self  :			Any       ,							SCREAMING_SNAKE_CASE_  :			float       )       ->     Any:
					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_       =					temperature




	def __call__(       self  :			Optional[int]       ,							SCREAMING_SNAKE_CASE_  :			jnp.ndarray       ,							SCREAMING_SNAKE_CASE_  :			jnp.ndarray       ,							SCREAMING_SNAKE_CASE_  :			int       )       ->     jnp.ndarray:
					lowercase_       =					scores / self.temperature
					return scores

class  lowercase__(				UpperCAmelCase							):



	"""simple docstring"""




	def __init__(       self  :			int       ,							SCREAMING_SNAKE_CASE_  :			float       ,							SCREAMING_SNAKE_CASE_  :			float = -float('''Inf'''       )       ,							SCREAMING_SNAKE_CASE_  :			int = 1       )       ->     Optional[int]:
					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_       =					top_p
					lowercase_       =					filter_value
					lowercase_       =					min_tokens_to_keep




	def __call__(       self  :			Tuple       ,							SCREAMING_SNAKE_CASE_  :			jnp.ndarray       ,							SCREAMING_SNAKE_CASE_  :			jnp.ndarray       ,							SCREAMING_SNAKE_CASE_  :			int       )       ->     jnp.ndarray:
					lowercase_  ,	lowercase_       =					lax.top_k(SCREAMING_SNAKE_CASE_       ,							scores.shape[-1]       )

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

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

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

					lowercase_       =					jnp.where(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       )
					lowercase_       =					jax.lax.sort_key_val(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       )[-1]

					return next_scores

class  lowercase__(				UpperCAmelCase							):



	"""simple docstring"""




	def __init__(       self  :			Dict       ,							SCREAMING_SNAKE_CASE_  :			int       ,							SCREAMING_SNAKE_CASE_  :			float = -float('''Inf'''       )       ,							SCREAMING_SNAKE_CASE_  :			int = 1       )       ->     Union[str, Any]:
					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_       =					max(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       )
					lowercase_       =					filter_value




	def __call__(       self  :			Union[str, Any]       ,							SCREAMING_SNAKE_CASE_  :			jnp.ndarray       ,							SCREAMING_SNAKE_CASE_  :			jnp.ndarray       ,							SCREAMING_SNAKE_CASE_  :			int       )       ->     jnp.ndarray:
					lowercase_  ,	lowercase_       =					scores.shape
					lowercase_       =					jnp.full(batch_size * vocab_size       ,							self.filter_value       )

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

					lowercase_       =					next_scores_flat.at[topk_indices_flat].set(SCREAMING_SNAKE_CASE_       )
					lowercase_       =					next_scores_flat.reshape(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       )
					return next_scores

class  lowercase__(				UpperCAmelCase							):



	"""simple docstring"""




	def __init__(       self  :			Union[str, Any]       ,							SCREAMING_SNAKE_CASE_  :			int       )       ->     List[str]:
					lowercase_       =					bos_token_id




	def __call__(       self  :			List[Any]       ,							SCREAMING_SNAKE_CASE_  :			jnp.ndarray       ,							SCREAMING_SNAKE_CASE_  :			jnp.ndarray       ,							SCREAMING_SNAKE_CASE_  :			int       )       ->     jnp.ndarray:
					lowercase_       =					jnp.full(scores.shape       ,							-float('''inf'''       )       )

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

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

					return scores

class  lowercase__(				UpperCAmelCase							):



	"""simple docstring"""




	def __init__(       self  :			str       ,							SCREAMING_SNAKE_CASE_  :			int       ,							SCREAMING_SNAKE_CASE_  :			int       )       ->     Any:
					lowercase_       =					max_length
					lowercase_       =					eos_token_id




	def __call__(       self  :			Union[str, Any]       ,							SCREAMING_SNAKE_CASE_  :			jnp.ndarray       ,							SCREAMING_SNAKE_CASE_  :			jnp.ndarray       ,							SCREAMING_SNAKE_CASE_  :			int       )       ->     jnp.ndarray:
					lowercase_       =					jnp.full(scores.shape       ,							-float('''inf'''       )       )

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

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

					return scores

class  lowercase__(				UpperCAmelCase							):



	"""simple docstring"""




	def __init__(       self  :			Any       ,							SCREAMING_SNAKE_CASE_  :			int       ,							SCREAMING_SNAKE_CASE_  :			int       )       ->     List[str]:
					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_       =					min_length
					lowercase_       =					eos_token_id




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

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

					return scores

class  lowercase__(				UpperCAmelCase							):



	"""simple docstring"""




	def __init__(       self  :			Tuple       ,							SCREAMING_SNAKE_CASE_  :			List[Any]       ,							SCREAMING_SNAKE_CASE_  :			Any       )       ->     str:
					lowercase_       =					list(SCREAMING_SNAKE_CASE_       )
					lowercase_       =					begin_index




	def __call__(       self  :			Union[str, Any]       ,							SCREAMING_SNAKE_CASE_  :			List[Any]       ,							SCREAMING_SNAKE_CASE_  :			Optional[int]       ,							SCREAMING_SNAKE_CASE_  :			int       )       ->     Optional[int]:
					lowercase_       =					1 - jnp.bool_(cur_len - self.begin_index       )

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

					return scores

class  lowercase__(				UpperCAmelCase							):



	"""simple docstring"""




	def __init__(       self  :			str       ,							SCREAMING_SNAKE_CASE_  :			list       )       ->     Tuple:
					lowercase_       =					list(SCREAMING_SNAKE_CASE_       )




	def __call__(       self  :			List[Any]       ,							SCREAMING_SNAKE_CASE_  :			jnp.ndarray       ,							SCREAMING_SNAKE_CASE_  :			jnp.ndarray       ,							SCREAMING_SNAKE_CASE_  :			int       )       ->     jnp.ndarray:
					lowercase_       =					scores.at[..., self.suppress_tokens].set(-float('''inf'''       )       )

					return scores

class  lowercase__(				UpperCAmelCase							):



	"""simple docstring"""




	def __init__(       self  :			Optional[Any]       ,							SCREAMING_SNAKE_CASE_  :			Dict       )       ->     str:
					lowercase_       =					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_       =					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_       =					force_token_array.at[index].set(SCREAMING_SNAKE_CASE_       )
					lowercase_       =					jnp.intaa(SCREAMING_SNAKE_CASE_       )




	def __call__(       self  :			str       ,							SCREAMING_SNAKE_CASE_  :			jnp.ndarray       ,							SCREAMING_SNAKE_CASE_  :			jnp.ndarray       ,							SCREAMING_SNAKE_CASE_  :			int       )       ->     jnp.ndarray:
					def _force_token(SCREAMING_SNAKE_CASE_  :			Optional[Any]       ):
									lowercase_       =					scores.shape[0]
									lowercase_       =					self.force_token_array[generation_idx]

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

					lowercase_       =					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  lowercase__(				UpperCAmelCase							):



	"""simple docstring"""




	def __init__(       self  :			List[Any]       ,							SCREAMING_SNAKE_CASE_  :			Any       ,							SCREAMING_SNAKE_CASE_  :			int       ,							SCREAMING_SNAKE_CASE_  :			Optional[int]       )       ->     Union[str, Any]:
					lowercase_       =					generate_config.eos_token_id
					lowercase_       =					generate_config.no_timestamps_token_id
					lowercase_       =					generate_config.no_timestamps_token_id + 1

					lowercase_       =					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_       =					generate_config.max_initial_timestamp_index
					else:
									lowercase_       =					model_config.vocab_size
					if self.max_initial_timestamp_index is None:
									lowercase_       =					model_config.vocab_size




	def __call__(       self  :			Union[str, Any]       ,							SCREAMING_SNAKE_CASE_  :			Dict       ,							SCREAMING_SNAKE_CASE_  :			int       ,							SCREAMING_SNAKE_CASE_  :			Union[str, Any]       )       ->     int:
					# suppress <|notimestamps|> which is handled by without_timestamps
					lowercase_       =					scores.at[:, self.no_timestamps_token_id].set(-float('''inf'''       )       )

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

									lowercase_       =					jnp.where((cur_len - self.begin_index) < 2       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       )
									lowercase_       =					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_       =					jax.vmap(SCREAMING_SNAKE_CASE_       )(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       )

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

					lowercase_       =					self.timestamp_begin + self.max_initial_timestamp_index

					lowercase_       =					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_       =					jax.nn.log_softmax(SCREAMING_SNAKE_CASE_       ,							axis=-1       )

					def handle_cumulative_probs(SCREAMING_SNAKE_CASE_  :			Any       ,							SCREAMING_SNAKE_CASE_  :			Tuple       ):
									lowercase_       =					jax.nn.logsumexp(logprobs_k[self.timestamp_begin :]       ,							axis=-1       )
									lowercase_       =					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_       =					jax.vmap(SCREAMING_SNAKE_CASE_       )(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       )

					return scores






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


__a  			=					{
    'configuration_trocr': ['TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TrOCRConfig'],
    'processing_trocr': ['TrOCRProcessor'],
}


try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a  			=					[
						    'TROCR_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'TrOCRForCausalLM',
						    'TrOCRPreTrainedModel',
						]


if TYPE_CHECKING:
						from .configuration_trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig
						from .processing_trocr import TrOCRProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel

else:
						import sys

						__a  			=					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




