
import argparse
from collections import defaultdict

import yaml


_A     =  "docs/source/en/_toctree.yml"






def      lowerCamelCase__       (       __lowerCAmelCase						:						str       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			defaultdict(__lowerCAmelCase       )
			lowerCAmelCase_      			=			[]
			lowerCAmelCase_      			=			[]
			for doc in doc_list:
						if "local" in doc:
									counts[doc["local"]] += 1

						if doc["title"].lower() == "overview":
									overview_doc.append({"local": doc["local"], "title": doc["title"]}       )
						else:
									new_doc_list.append(__lowerCAmelCase       )

			lowerCAmelCase_      			=			new_doc_list
			lowerCAmelCase_      			=			[key for key, value in counts.items() if value > 1]

			lowerCAmelCase_      			=			[]
			for duplicate_key in duplicates:
						lowerCAmelCase_      			=			list({doc["title"] for doc in doc_list if doc["local"] == duplicate_key}       )
						if len(__lowerCAmelCase       ) > 1:
									raise ValueError(
									    F"""{duplicate_key} is present several times in the documentation table of content at """
									    "`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the "
									    "others."       )
						# Only add this once
						new_doc.append({"local": duplicate_key, "title": titles[0]}       )

			# Add none duplicate-keys
			new_doc.extend([doc for doc in doc_list if "local" not in counts or counts[doc["local"]] == 1]       )
			lowerCAmelCase_      			=			sorted(__lowerCAmelCase					,							key=lambda __lowerCAmelCase       : s["title"].lower()       )

			# "overview" gets special treatment and is always first
			if len(__lowerCAmelCase       ) > 1:
						raise ValueError("{doc_list} has two 'overview' docs which is not allowed."       )

			overview_doc.extend(__lowerCAmelCase       )

			# Sort
			return overview_doc






def      lowerCamelCase__       (       __lowerCAmelCase						:						Optional[int]=False       ):




			"""simple docstring"""


			with open(__lowerCAmelCase					,							encoding="utf-8"       ) as f:
						lowerCAmelCase_      			=			yaml.safe_load(f.read()       )

			# Get to the API doc
			lowerCAmelCase_      			=			0
			while content[api_idx]["title"] != "API":
						api_idx += 1
			lowerCAmelCase_      			=			content[api_idx]["sections"]

			# Then to the model doc
			lowerCAmelCase_      			=			0
			while api_doc[scheduler_idx]["title"] != "Schedulers":
						scheduler_idx += 1

			lowerCAmelCase_      			=			api_doc[scheduler_idx]["sections"]
			lowerCAmelCase_      			=			clean_doc_toc(__lowerCAmelCase       )

			lowerCAmelCase_      			=			False
			if new_scheduler_doc != scheduler_doc:
						lowerCAmelCase_      			=			True
						if overwrite:
									lowerCAmelCase_      			=			new_scheduler_doc

			if diff:
						if overwrite:
									lowerCAmelCase_      			=			api_doc
									with open(__lowerCAmelCase					,							"w"					,							encoding="utf-8"       ) as f:
												f.write(yaml.dump(__lowerCAmelCase					,							allow_unicode=__lowerCAmelCase       )       )
						else:
									raise ValueError(
									    "The model doc part of the table of content is not properly sorted, run `make style` to fix this."       )






def      lowerCamelCase__       (       __lowerCAmelCase						:						Tuple=False       ):




			"""simple docstring"""


			with open(__lowerCAmelCase					,							encoding="utf-8"       ) as f:
						lowerCAmelCase_      			=			yaml.safe_load(f.read()       )

			# Get to the API doc
			lowerCAmelCase_      			=			0
			while content[api_idx]["title"] != "API":
						api_idx += 1
			lowerCAmelCase_      			=			content[api_idx]["sections"]

			# Then to the model doc
			lowerCAmelCase_      			=			0
			while api_doc[pipeline_idx]["title"] != "Pipelines":
						pipeline_idx += 1

			lowerCAmelCase_      			=			False
			lowerCAmelCase_      			=			api_doc[pipeline_idx]["sections"]
			lowerCAmelCase_      			=			[]

			# sort sub pipeline docs
			for pipeline_doc in pipeline_docs:
						if "section" in pipeline_doc:
									lowerCAmelCase_      			=			pipeline_doc["section"]
									lowerCAmelCase_      			=			clean_doc_toc(__lowerCAmelCase       )
									if overwrite:
												lowerCAmelCase_      			=			new_sub_pipeline_doc
						new_pipeline_docs.append(__lowerCAmelCase       )

			# sort overall pipeline doc
			lowerCAmelCase_      			=			clean_doc_toc(__lowerCAmelCase       )

			if new_pipeline_docs != pipeline_docs:
						lowerCAmelCase_      			=			True
						if overwrite:
									lowerCAmelCase_      			=			new_pipeline_docs

			if diff:
						if overwrite:
									lowerCAmelCase_      			=			api_doc
									with open(__lowerCAmelCase					,							"w"					,							encoding="utf-8"       ) as f:
												f.write(yaml.dump(__lowerCAmelCase					,							allow_unicode=__lowerCAmelCase       )       )
						else:
									raise ValueError(
									    "The model doc part of the table of content is not properly sorted, run `make style` to fix this."       )


if __name__ == "__main__":
			_A     =  argparse.ArgumentParser()
			parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
			_A     =  parser.parse_args()

			check_scheduler_doc(args.fix_and_overwrite)
			check_pipeline_doc(args.fix_and_overwrite)





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



