




import unittest

import numpy as np

from transformers import is_flax_available
from transformers.testing_utils import require_flax

from ..test_modeling_flax_common import ids_tensor


if is_flax_available():
       import jax
       import jax.numpy as jnp

       from transformers.generation import (
           FlaxForcedBOSTokenLogitsProcessor,
           FlaxForcedEOSTokenLogitsProcessor,
           FlaxLogitsProcessorList,
           FlaxMinLengthLogitsProcessor,
           FlaxTemperatureLogitsWarper,
           FlaxTopKLogitsWarper,
           FlaxTopPLogitsWarper,
       )




@require_flax
class 		UpperCamelCase__	(unittest.TestCase     ):

    '''simple docstring'''



    def     _lowercase     (    self     ,  UpperCamelCase__     ,  UpperCamelCase__     )     ->      List[str]:
           lowerCamelCase   :      str			 =					jnp.ones((batch_size, length)     ) / length
           return scores







    def     _lowercase     (    self     )     ->      Tuple:
           lowerCamelCase   :      Optional[int]			 =					None
           lowerCamelCase   :      Optional[int]			 =					20

           lowerCamelCase   :      Dict			 =					self._get_uniform_logits(batch_size=2     ,  length=UpperCamelCase__     )

           # tweak scores to not be uniform anymore
           lowerCamelCase   :      Union[str, Any]			 =					scores.at[1, 5].set((1 / length) + 0.1     )  # peak, 1st batch
           lowerCamelCase   :      int			 =					scores.at[1, 10].set((1 / length) - 0.4     )  # valley, 1st batch

           # compute softmax
           lowerCamelCase   :      Any			 =					jax.nn.softmax(UpperCamelCase__     ,  axis=-1     )

           lowerCamelCase   :      Optional[Any]			 =					FlaxTemperatureLogitsWarper(temperature=0.5     )
           lowerCamelCase   :      List[Any]			 =					FlaxTemperatureLogitsWarper(temperature=1.3     )

           lowerCamelCase   :      Dict			 =					jax.nn.softmax(temp_dist_warper_sharper(UpperCamelCase__     ,  scores.copy()     ,  cur_len=UpperCamelCase__     )     ,  axis=-1     )
           lowerCamelCase   :      Any			 =					jax.nn.softmax(temp_dist_warper_smoother(UpperCamelCase__     ,  scores.copy()     ,  cur_len=UpperCamelCase__     )     ,  axis=-1     )

           # uniform distribution stays uniform
           self.assertTrue(jnp.allclose(probs[0, :]     ,  warped_prob_sharp[0, :]     ,  atol=1e-3     )     )
           self.assertTrue(jnp.allclose(probs[0, :]     ,  warped_prob_smooth[0, :]     ,  atol=1e-3     )     )

           # sharp peaks get higher, valleys get lower
           self.assertLess(probs[1, :].max()     ,  warped_prob_sharp[1, :].max()     )
           self.assertGreater(probs[1, :].min()     ,  warped_prob_sharp[1, :].min()     )

           # smooth peaks get lower, valleys get higher
           self.assertGreater(probs[1, :].max()     ,  warped_prob_smooth[1, :].max()     )
           self.assertLess(probs[1, :].min()     ,  warped_prob_smooth[1, :].min()     )







    def     _lowercase     (    self     )     ->      Any:
           lowerCamelCase   :      Dict			 =					None
           lowerCamelCase   :      Tuple			 =					10
           lowerCamelCase   :      Dict			 =					2

           # create ramp distribution
           lowerCamelCase   :      str			 =					np.broadcast_to(np.arange(UpperCamelCase__     )[None, :]     ,  (batch_size, vocab_size)     ).copy()
           lowerCamelCase   :      str			 =					ramp_logits[1:, : vocab_size // 2] + vocab_size

           lowerCamelCase   :      Dict			 =					FlaxTopKLogitsWarper(3     )

           lowerCamelCase   :      str			 =					top_k_warp(UpperCamelCase__     ,  UpperCamelCase__     ,  cur_len=UpperCamelCase__     )

           # check that correct tokens are filtered
           self.assertListEqual(jnp.isinf(scores[0]     ).tolist()     ,  7 * [True] + 3 * [False]     )
           self.assertListEqual(jnp.isinf(scores[1]     ).tolist()     ,  2 * [True] + 3 * [False] + 5 * [True]     )

           # check special case
           lowerCamelCase   :      int			 =					5
           lowerCamelCase   :      List[Any]			 =					FlaxTopKLogitsWarper(top_k=1     ,  filter_value=0.0     ,  min_tokens_to_keep=3     )

           lowerCamelCase   :      int			 =					np.broadcast_to(np.arange(UpperCamelCase__     )[None, :]     ,  (batch_size, length)     ).copy()
           lowerCamelCase   :      List[str]			 =					top_k_warp_safety_check(UpperCamelCase__     ,  UpperCamelCase__     ,  cur_len=UpperCamelCase__     )

           # min_tokens overwrites k: 3 tokens are kept => 2 tokens are nullified
           self.assertListEqual((scores == 0.0).sum(axis=-1     ).tolist()     ,  [2, 2]     )







    def     _lowercase     (    self     )     ->      List[Any]:
           lowerCamelCase   :      Union[str, Any]			 =					None
           lowerCamelCase   :      int			 =					10
           lowerCamelCase   :      Optional[Any]			 =					2

           # create distribution and take log (inverse to Softmax as taken in TopPLogitsWarper)
           lowerCamelCase   :      Optional[Any]			 =					np.log(np.array([[0.3, 0.1, 0.1, 0.5], [0.15, 0.3, 0.3, 0.25]]     )     )

           lowerCamelCase   :      Any			 =					FlaxTopPLogitsWarper(0.8     )
           lowerCamelCase   :      Optional[int]			 =					np.exp(top_p_warp(UpperCamelCase__     ,  UpperCamelCase__     ,  cur_len=UpperCamelCase__     )     )

           # dist should be filtered to keep min num values so that sum is >= top_p
           # exp (-inf) => 0
           lowerCamelCase   :      str			 =					np.array([[0.3, 0.0, 0.0, 0.5], [0.0, 0.3, 0.3, 0.25]]     )
           self.assertTrue(np.allclose(UpperCamelCase__     ,  UpperCamelCase__     ,  atol=1e-3     )     )

           # check edge cases with negative and extreme logits
           lowerCamelCase   :      Optional[int]			 =					np.broadcast_to(np.arange(UpperCamelCase__     )[None, :]     ,  (batch_size, vocab_size)     ).copy() - (
               vocab_size // 2
           )

           # make ramp_logits more extreme
           lowerCamelCase   :      int			 =					ramp_logits[1] * 100.0

           # make sure at least 2 tokens are kept
           lowerCamelCase   :      Union[str, Any]			 =					FlaxTopPLogitsWarper(0.9     ,  min_tokens_to_keep=2     ,  filter_value=0.0     )
           lowerCamelCase   :      Optional[int]			 =					top_p_warp(UpperCamelCase__     ,  UpperCamelCase__     ,  cur_len=UpperCamelCase__     )

           # first batch should keep three tokens, second batch would keep only 1, but due to `min_tokens_to_keep=2` keeps 2.
           self.assertListEqual((filtered_dist != 0.0).sum(axis=-1     ).tolist()     ,  [3, 2]     )







    def     _lowercase     (    self     )     ->      Union[str, Any]:
           lowerCamelCase   :      List[str]			 =					20
           lowerCamelCase   :      List[str]			 =					4
           lowerCamelCase   :      List[str]			 =					0

           lowerCamelCase   :      Dict			 =					FlaxMinLengthLogitsProcessor(min_length=10     ,  eos_token_id=UpperCamelCase__     )

           # check that min length is applied at length 5
           lowerCamelCase   :      Dict			 =					ids_tensor((batch_size, 20)     ,  vocab_size=20     )
           lowerCamelCase   :      Tuple			 =					5
           lowerCamelCase   :      Optional[Any]			 =					self._get_uniform_logits(UpperCamelCase__     ,  UpperCamelCase__     )
           lowerCamelCase   :      Optional[int]			 =					min_dist_processor(UpperCamelCase__     ,  UpperCamelCase__     ,  cur_len=UpperCamelCase__     )
           self.assertListEqual(scores_before_min_length[:, eos_token_id].tolist()     ,  4 * [-float("inf"     )]     )

           # check that min length is not applied anymore at length 15
           lowerCamelCase   :      Optional[Any]			 =					self._get_uniform_logits(UpperCamelCase__     ,  UpperCamelCase__     )
           lowerCamelCase   :      str			 =					15
           lowerCamelCase   :      List[Any]			 =					min_dist_processor(UpperCamelCase__     ,  UpperCamelCase__     ,  cur_len=UpperCamelCase__     )
           self.assertFalse(jnp.isinf(UpperCamelCase__     ).any()     )







    def     _lowercase     (    self     )     ->      str:
           lowerCamelCase   :      List[str]			 =					20
           lowerCamelCase   :      List[Any]			 =					4
           lowerCamelCase   :      str			 =					0

           lowerCamelCase   :      List[str]			 =					FlaxForcedBOSTokenLogitsProcessor(bos_token_id=UpperCamelCase__     )

           # check that all scores are -inf except the bos_token_id score
           lowerCamelCase   :      Any			 =					ids_tensor((batch_size, 1)     ,  vocab_size=20     )
           lowerCamelCase   :      str			 =					1
           lowerCamelCase   :      Tuple			 =					self._get_uniform_logits(UpperCamelCase__     ,  UpperCamelCase__     )
           lowerCamelCase   :      int			 =					logits_processor(UpperCamelCase__     ,  UpperCamelCase__     ,  cur_len=UpperCamelCase__     )
           self.assertTrue(jnp.isneginf(scores[:, bos_token_id + 1 :]     ).all()     )
           self.assertListEqual(scores[:, bos_token_id].tolist()     ,  4 * [0]     )  # score for bos_token_id shold be zero

           # check that bos_token_id is not forced if current length is greater than 1
           lowerCamelCase   :      List[str]			 =					3
           lowerCamelCase   :      Optional[Any]			 =					self._get_uniform_logits(UpperCamelCase__     ,  UpperCamelCase__     )
           lowerCamelCase   :      Optional[Any]			 =					logits_processor(UpperCamelCase__     ,  UpperCamelCase__     ,  cur_len=UpperCamelCase__     )
           self.assertFalse(jnp.isinf(UpperCamelCase__     ).any()     )







    def     _lowercase     (    self     )     ->      List[str]:
           lowerCamelCase   :      Dict			 =					20
           lowerCamelCase   :      Optional[int]			 =					4
           lowerCamelCase   :      int			 =					0
           lowerCamelCase   :      Optional[int]			 =					5

           lowerCamelCase   :      int			 =					FlaxForcedEOSTokenLogitsProcessor(max_length=UpperCamelCase__     ,  eos_token_id=UpperCamelCase__     )

           # check that all scores are -inf except the eos_token_id when max_length is reached
           lowerCamelCase   :      Tuple			 =					ids_tensor((batch_size, 4)     ,  vocab_size=20     )
           lowerCamelCase   :      Optional[int]			 =					4
           lowerCamelCase   :      int			 =					self._get_uniform_logits(UpperCamelCase__     ,  UpperCamelCase__     )
           lowerCamelCase   :      Tuple			 =					logits_processor(UpperCamelCase__     ,  UpperCamelCase__     ,  cur_len=UpperCamelCase__     )
           self.assertTrue(jnp.isneginf(scores[:, eos_token_id + 1 :]     ).all()     )
           self.assertListEqual(scores[:, eos_token_id].tolist()     ,  4 * [0]     )  # score for eos_token_id should be zero

           # check that eos_token_id is not forced if max_length is not reached
           lowerCamelCase   :      int			 =					3
           lowerCamelCase   :      List[str]			 =					self._get_uniform_logits(UpperCamelCase__     ,  UpperCamelCase__     )
           lowerCamelCase   :      Dict			 =					logits_processor(UpperCamelCase__     ,  UpperCamelCase__     ,  cur_len=UpperCamelCase__     )
           self.assertFalse(jnp.isinf(UpperCamelCase__     ).any()     )







    def     _lowercase     (    self     )     ->      Any:
           lowerCamelCase   :      List[str]			 =					4
           lowerCamelCase   :      Union[str, Any]			 =					10
           lowerCamelCase   :      Dict			 =					15
           lowerCamelCase   :      int			 =					2
           lowerCamelCase   :      List[str]			 =					1
           lowerCamelCase   :      List[str]			 =					15

           # dummy input_ids and scores
           lowerCamelCase   :      Dict			 =					ids_tensor((batch_size, sequence_length)     ,  UpperCamelCase__     )
           lowerCamelCase   :      Tuple			 =					input_ids.copy()

           lowerCamelCase   :      Any			 =					self._get_uniform_logits(UpperCamelCase__     ,  UpperCamelCase__     )
           lowerCamelCase   :      str			 =					scores.copy()

           # instantiate all dist processors
           lowerCamelCase   :      Dict			 =					FlaxTemperatureLogitsWarper(temperature=0.5     )
           lowerCamelCase   :      Dict			 =					FlaxTopKLogitsWarper(3     )
           lowerCamelCase   :      Tuple			 =					FlaxTopPLogitsWarper(0.8     )

           # instantiate all logits processors
           lowerCamelCase   :      List[Any]			 =					FlaxMinLengthLogitsProcessor(min_length=10     ,  eos_token_id=UpperCamelCase__     )
           lowerCamelCase   :      Optional[Any]			 =					FlaxForcedBOSTokenLogitsProcessor(bos_token_id=UpperCamelCase__     )
           lowerCamelCase   :      Union[str, Any]			 =					FlaxForcedEOSTokenLogitsProcessor(max_length=UpperCamelCase__     ,  eos_token_id=UpperCamelCase__     )

           lowerCamelCase   :      Union[str, Any]			 =					10

           # no processor list
           lowerCamelCase   :      Any			 =					temp_dist_warp(UpperCamelCase__     ,  UpperCamelCase__     ,  cur_len=UpperCamelCase__     )
           lowerCamelCase   :      Tuple			 =					top_k_warp(UpperCamelCase__     ,  UpperCamelCase__     ,  cur_len=UpperCamelCase__     )
           lowerCamelCase   :      Dict			 =					top_p_warp(UpperCamelCase__     ,  UpperCamelCase__     ,  cur_len=UpperCamelCase__     )
           lowerCamelCase   :      List[str]			 =					min_dist_proc(UpperCamelCase__     ,  UpperCamelCase__     ,  cur_len=UpperCamelCase__     )
           lowerCamelCase   :      Optional[int]			 =					bos_dist_proc(UpperCamelCase__     ,  UpperCamelCase__     ,  cur_len=UpperCamelCase__     )
           lowerCamelCase   :      Dict			 =					eos_dist_proc(UpperCamelCase__     ,  UpperCamelCase__     ,  cur_len=UpperCamelCase__     )

           # with processor list
           lowerCamelCase   :      List[Any]			 =					FlaxLogitsProcessorList(
               [temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc]     )
           lowerCamelCase   :      int			 =					processor(UpperCamelCase__     ,  UpperCamelCase__     ,  cur_len=UpperCamelCase__     )

           # scores should be equal
           self.assertTrue(jnp.allclose(UpperCamelCase__     ,  UpperCamelCase__     ,  atol=1e-3     )     )

           # input_ids should never be changed
           self.assertListEqual(input_ids.tolist()     ,  input_ids_comp.tolist()     )







    def     _lowercase     (    self     )     ->      Union[str, Any]:
           lowerCamelCase   :      List[str]			 =					4
           lowerCamelCase   :      int			 =					10
           lowerCamelCase   :      List[str]			 =					15
           lowerCamelCase   :      Optional[Any]			 =					2
           lowerCamelCase   :      Optional[int]			 =					1
           lowerCamelCase   :      Any			 =					15

           # dummy input_ids and scores
           lowerCamelCase   :      Dict			 =					ids_tensor((batch_size, sequence_length)     ,  UpperCamelCase__     )
           lowerCamelCase   :      Optional[Any]			 =					input_ids.copy()

           lowerCamelCase   :      List[Any]			 =					self._get_uniform_logits(UpperCamelCase__     ,  UpperCamelCase__     )
           lowerCamelCase   :      str			 =					scores.copy()

           # instantiate all dist processors
           lowerCamelCase   :      Optional[int]			 =					FlaxTemperatureLogitsWarper(temperature=0.5     )
           lowerCamelCase   :      Tuple			 =					FlaxTopKLogitsWarper(3     )
           lowerCamelCase   :      str			 =					FlaxTopPLogitsWarper(0.8     )

           # instantiate all logits processors
           lowerCamelCase   :      List[str]			 =					FlaxMinLengthLogitsProcessor(min_length=10     ,  eos_token_id=UpperCamelCase__     )
           lowerCamelCase   :      int			 =					FlaxForcedBOSTokenLogitsProcessor(bos_token_id=UpperCamelCase__     )
           lowerCamelCase   :      Optional[Any]			 =					FlaxForcedEOSTokenLogitsProcessor(max_length=UpperCamelCase__     ,  eos_token_id=UpperCamelCase__     )

           lowerCamelCase   :      Optional[Any]			 =					10

           # no processor list
           def run_no_processor_list(UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ):
                  lowerCamelCase   :      Tuple			 =					temp_dist_warp(UpperCamelCase__     ,  UpperCamelCase__     ,  cur_len=UpperCamelCase__     )
                  lowerCamelCase   :      Union[str, Any]			 =					top_k_warp(UpperCamelCase__     ,  UpperCamelCase__     ,  cur_len=UpperCamelCase__     )
                  lowerCamelCase   :      Optional[Any]			 =					top_p_warp(UpperCamelCase__     ,  UpperCamelCase__     ,  cur_len=UpperCamelCase__     )
                  lowerCamelCase   :      Dict			 =					min_dist_proc(UpperCamelCase__     ,  UpperCamelCase__     ,  cur_len=UpperCamelCase__     )
                  lowerCamelCase   :      List[str]			 =					bos_dist_proc(UpperCamelCase__     ,  UpperCamelCase__     ,  cur_len=UpperCamelCase__     )
                  lowerCamelCase   :      Tuple			 =					eos_dist_proc(UpperCamelCase__     ,  UpperCamelCase__     ,  cur_len=UpperCamelCase__     )
                  return scores

           # with processor list
           def run_processor_list(UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ):
                  lowerCamelCase   :      Optional[int]			 =					FlaxLogitsProcessorList(
                      [temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc]     )
                  lowerCamelCase   :      Optional[Any]			 =					processor(UpperCamelCase__     ,  UpperCamelCase__     ,  cur_len=UpperCamelCase__     )
                  return scores

           lowerCamelCase   :      List[Any]			 =					jax.jit(UpperCamelCase__     )
           lowerCamelCase   :      Optional[int]			 =					jax.jit(UpperCamelCase__     )

           lowerCamelCase   :      Optional[Any]			 =					jitted_run_no_processor_list(UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     )
           lowerCamelCase   :      Dict			 =					jitted_run_processor_list(UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     )

           # scores should be equal
           self.assertTrue(jnp.allclose(UpperCamelCase__     ,  UpperCamelCase__     ,  atol=1e-3     )     )

           # input_ids should never be changed
           self.assertListEqual(input_ids.tolist()     ,  input_ids_comp.tolist()     )












import argparse
from pathlib import Path

from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration

def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = None ,)	-> List[str]:
       if config_name_or_path is None:
              lowerCamelCase   :      Any			 =					"facebook/rag-token-base" if model_type == "rag_token" else "facebook/rag-sequence-base"

       if generator_tokenizer_name_or_path is None:
              lowerCamelCase   :      Dict			 =					generator_name_or_path

       if question_encoder_tokenizer_name_or_path is None:
              lowerCamelCase   :      Any			 =					question_encoder_name_or_path

       lowerCamelCase   :      str			 =					RagTokenForGeneration if model_type == "rag_token" else RagSequenceForGeneration

       # Save model.
       lowerCamelCase   :      List[Any]			 =					RagConfig.from_pretrained(_SCREAMING_SNAKE_CASE    )
       lowerCamelCase   :      Union[str, Any]			 =					AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE    )
       lowerCamelCase   :      Optional[int]			 =					AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE    )

       lowerCamelCase   :      Optional[Any]			 =					gen_config
       lowerCamelCase   :      Optional[Any]			 =					question_encoder_config

       lowerCamelCase   :      List[Any]			 =					model_class.from_pretrained_question_encoder_generator(
           _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,config=_SCREAMING_SNAKE_CASE    )
       rag_model.save_pretrained(_SCREAMING_SNAKE_CASE    )

       # Sanity check.
       model_class.from_pretrained(_SCREAMING_SNAKE_CASE    )

       # Save tokenizers.
       lowerCamelCase   :      List[str]			 =					AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE    )
       gen_tokenizer.save_pretrained(dest_dir / "generator_tokenizer/"    )
       lowerCamelCase   :      int			 =					AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE    )
       question_encoder_tokenizer.save_pretrained(dest_dir / "question_encoder_tokenizer/"    )


if __name__ == "__main__":
       SCREAMING_SNAKE_CASE__       :      Any  =   argparse.ArgumentParser()
       parser.add_argument(
           '--model_type',
           choices=['rag_sequence', 'rag_token'],
           required=True,
           type=str,
           help='RAG model type: rag_sequence, rag_token',
       )
       parser.add_argument('--dest', type=str, required=True, help='Path to the output checkpoint directory.')
       parser.add_argument('--generator_name_or_path', type=str, required=True, help='Generator model identifier')
       parser.add_argument(
           '--question_encoder_name_or_path', type=str, required=True, help='Question encoder model identifier'
       )

       parser.add_argument(
           '--generator_tokenizer_name_or_path',
           type=str,
           help='Generator tokenizer identifier, if not specified, resolves to ``generator_name_or_path``',
       )
       parser.add_argument(
           '--question_encoder_tokenizer_name_or_path',
           type=str,
           help='Question encoder tokenizer identifier, if not specified, resolves to ``question_encoder_name_or_path``',
       )
       parser.add_argument(
           '--config_name_or_path',
           type=str,
           help=(
               'Identifier of the model config to use, if not provided, resolves to a base config for a given'
               ' ``model_type``'
           ),
       )

       SCREAMING_SNAKE_CASE__       :      Union[str, Any]  =   parser.parse_args()

       SCREAMING_SNAKE_CASE__       :      Optional[Any]  =   Path(args.dest)
       dest_dir.mkdir(exist_ok=True)

       consolidate(
           args.model_type,
           args.generator_name_or_path,
           args.question_encoder_name_or_path,
           dest_dir,
           args.config_name_or_path,
           args.generator_tokenizer_name_or_path,
           args.question_encoder_tokenizer_name_or_path,
       )






