





import io
import json
import unittest

from parameterized import parameterized

from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu


__A									=				get_tests_dir() + "/test_data/fsmt/fsmt_val_data.json"
with io.open(filename, "r", encoding="utf-8") as f:
     __A									=				json.load(f)



@require_torch
class    _SCREAMING_SNAKE_CASE						(	unittest.TestCase    ):







 '''simple docstring'''

 def 							SCREAMING_SNAKE_CASE_					(self				:   Any       , UpperCAmelCase_				:   Optional[int])       ->str:



   '''simple docstring'''



   return FSMTTokenizer.from_pretrained(UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_					(self				:   Dict       , UpperCAmelCase_				:   int)       ->List[Any]:



   '''simple docstring'''



   lowerCamelCase__:      Dict			  =FSMTForConditionalGeneration.from_pretrained(UpperCAmelCase_).to(UpperCAmelCase_)
   if torch_device == "cuda":
     model.half()
   return model







 @parameterized.expand(
     [
         ["en-ru", 26.0],
         ["ru-en", 22.0],
         ["en-de", 22.0],
         ["de-en", 29.0],
     ])
 @slow
 def 							SCREAMING_SNAKE_CASE_					(self				:   Optional[Any]       , UpperCAmelCase_				:   List[str]       , UpperCAmelCase_				:   int)       ->Tuple:



   '''simple docstring'''



   lowerCamelCase__:      Tuple			  =F"""facebook/wmt19-{pair}"""
   lowerCamelCase__:      str			  =self.get_tokenizer(UpperCAmelCase_)
   lowerCamelCase__:      Optional[Any]			  =self.get_model(UpperCAmelCase_)

   lowerCamelCase__:      Optional[Any]			  =bleu_data[pair]["src"]
   lowerCamelCase__:      Optional[int]			  =bleu_data[pair]["tgt"]

   lowerCamelCase__:      Union[str, Any]			  =tokenizer(UpperCAmelCase_       , return_tensors="pt"       , truncation=UpperCAmelCase_       , padding="longest").to(UpperCAmelCase_)
   lowerCamelCase__:      List[Any]			  =model.generate(
       input_ids=batch.input_ids       , num_beams=8       , )
   lowerCamelCase__:      Union[str, Any]			  =tokenizer.batch_decode(
       UpperCAmelCase_       , skip_special_tokens=UpperCAmelCase_       , clean_up_tokenization_spaces=UpperCAmelCase_)
   lowerCamelCase__:      str			  =calculate_bleu(UpperCAmelCase_       , UpperCAmelCase_)
   print(UpperCAmelCase_)
   self.assertGreaterEqual(scores["bleu"]       , UpperCAmelCase_)



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


_UpperCAmelCase					: List[Any]      =       get_logger(__name__)


_UpperCAmelCase					: Tuple      =       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					:



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






class 				lowercase					:



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






class 				lowercase					(		_SCREAMING_SNAKE_CASE   ):



		@add_start_docstrings(A_       )
		def __call__( self				,      A_				,      A_				,      A_				,      **A_       )  ->					jnp.ndarray:
				"""simple docstring"""
				for processor in self:
						UpperCamelCase		=     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.'''       )
								UpperCamelCase		=     processor(A_				,      A_				,      A_				,      **A_       )
						else:
								UpperCamelCase		=     processor(A_				,      A_				,      A_       )
				return scores






class 				lowercase					(		_SCREAMING_SNAKE_CASE   ):



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

				UpperCamelCase		=     temperature



		def __call__( self				,      A_				,      A_				,      A_       )  ->					jnp.ndarray:
				"""simple docstring"""
				UpperCamelCase		=     scores / self.temperature
				return scores






class 				lowercase					(		_SCREAMING_SNAKE_CASE   ):



		def __init__( self				,      A_				,      A_ = -float('Inf'       )				,      A_ = 1       )  ->					List[Any]:
				"""simple docstring"""
				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}'''       )

				UpperCamelCase		=     top_p
				UpperCamelCase		=     filter_value
				UpperCamelCase		=     min_tokens_to_keep



		def __call__( self				,      A_				,      A_				,      A_       )  ->					jnp.ndarray:
				"""simple docstring"""
				UpperCamelCase	,  UpperCamelCase		=     lax.top_k(A_				,      scores.shape[-1]       )

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

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

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

				UpperCamelCase		=     jnp.where(A_				,      A_				,      A_       )
				UpperCamelCase		=     jax.lax.sort_key_val(A_				,      A_       )[-1]

				return next_scores






class 				lowercase					(		_SCREAMING_SNAKE_CASE   ):



		def __init__( self				,      A_				,      A_ = -float('Inf'       )				,      A_ = 1       )  ->					List[str]:
				"""simple docstring"""
				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}'''       )

				UpperCamelCase		=     max(A_				,      A_       )
				UpperCamelCase		=     filter_value



		def __call__( self				,      A_				,      A_				,      A_       )  ->					jnp.ndarray:
				"""simple docstring"""
				UpperCamelCase	,  UpperCamelCase		=     scores.shape
				UpperCamelCase		=     jnp.full(batch_size * vocab_size				,      self.filter_value       )

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

				UpperCamelCase		=     next_scores_flat.at[topk_indices_flat].set(A_       )
				UpperCamelCase		=     next_scores_flat.reshape(A_				,      A_       )
				return next_scores






class 				lowercase					(		_SCREAMING_SNAKE_CASE   ):



		def __init__( self				,      A_       )  ->					List[Any]:
				"""simple docstring"""
				UpperCamelCase		=     bos_token_id



		def __call__( self				,      A_				,      A_				,      A_       )  ->					jnp.ndarray:
				"""simple docstring"""
				UpperCamelCase		=     jnp.full(scores.shape				,      -float('inf'       )       )

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

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

				return scores






class 				lowercase					(		_SCREAMING_SNAKE_CASE   ):



		def __init__( self				,      A_				,      A_       )  ->					Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase		=     max_length
				UpperCamelCase		=     eos_token_id



		def __call__( self				,      A_				,      A_				,      A_       )  ->					jnp.ndarray:
				"""simple docstring"""
				UpperCamelCase		=     jnp.full(scores.shape				,      -float('inf'       )       )

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

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

				return scores






class 				lowercase					(		_SCREAMING_SNAKE_CASE   ):



		def __init__( self				,      A_				,      A_       )  ->					Optional[Any]:
				"""simple docstring"""
				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}'''       )

				UpperCamelCase		=     min_length
				UpperCamelCase		=     eos_token_id



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

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

				return scores






class 				lowercase					(		_SCREAMING_SNAKE_CASE   ):



		def __init__( self				,      A_				,      A_       )  ->					str:
				"""simple docstring"""
				UpperCamelCase		=     list(A_       )
				UpperCamelCase		=     begin_index



		def __call__( self				,      A_				,      A_				,      A_       )  ->					Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase		=     1 - jnp.bool_(cur_len - self.begin_index       )

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

				return scores






class 				lowercase					(		_SCREAMING_SNAKE_CASE   ):



		def __init__( self				,      A_       )  ->					List[Any]:
				"""simple docstring"""
				UpperCamelCase		=     list(A_       )



		def __call__( self				,      A_				,      A_				,      A_       )  ->					jnp.ndarray:
				"""simple docstring"""
				UpperCamelCase		=     scores.at[..., self.suppress_tokens].set(-float('inf'       )       )

				return scores






class 				lowercase					(		_SCREAMING_SNAKE_CASE   ):



		def __init__( self				,      A_       )  ->					str:
				"""simple docstring"""
				UpperCamelCase		=     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.
				UpperCamelCase		=     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:
								UpperCamelCase		=     force_token_array.at[index].set(A_       )
				UpperCamelCase		=     jnp.intaa(A_       )



		def __call__( self				,      A_				,      A_				,      A_       )  ->					jnp.ndarray:
				"""simple docstring"""
				def _force_token(A_       ):
						UpperCamelCase		=     scores.shape[0]
						UpperCamelCase		=     self.force_token_array[generation_idx]

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

				UpperCamelCase		=     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 				lowercase					(		_SCREAMING_SNAKE_CASE   ):



		def __init__( self				,      A_				,      A_				,      A_       )  ->					str:
				"""simple docstring"""
				UpperCamelCase		=     generate_config.eos_token_id
				UpperCamelCase		=     generate_config.no_timestamps_token_id
				UpperCamelCase		=     generate_config.no_timestamps_token_id + 1

				UpperCamelCase		=     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'       ):
						UpperCamelCase		=     generate_config.max_initial_timestamp_index
				else:
						UpperCamelCase		=     model_config.vocab_size
				if self.max_initial_timestamp_index is None:
						UpperCamelCase		=     model_config.vocab_size



		def __call__( self				,      A_				,      A_				,      A_       )  ->					Dict:
				"""simple docstring"""
				# suppress <|notimestamps|> which is handled by without_timestamps
				UpperCamelCase		=     scores.at[:, self.no_timestamps_token_id].set(-float('inf'       )       )

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

						UpperCamelCase		=     jnp.where((cur_len - self.begin_index) < 2				,      A_				,      A_       )
						UpperCamelCase		=     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_				,      )

				UpperCamelCase		=     jax.vmap(A_       )(A_				,      A_       )

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

				UpperCamelCase		=     self.timestamp_begin + self.max_initial_timestamp_index

				UpperCamelCase		=     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
				UpperCamelCase		=     jax.nn.log_softmax(A_				,      axis=-1       )

				def handle_cumulative_probs(A_				,      A_       ):
						UpperCamelCase		=     jax.nn.logsumexp(logprobs_k[self.timestamp_begin :]				,      axis=-1       )
						UpperCamelCase		=     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_				,      )

				UpperCamelCase		=     jax.vmap(A_       )(A_				,      A_       )

				return scores
