
import json
import logging
import os
import sys
from pathlib import Path

import finetune_rag

from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    require_ray,
    require_torch_gpu,
    require_torch_multi_gpu,
)


logging.basicConfig(level=logging.DEBUG)
lowerCamelCase_						     =					logging.getLogger()

lowerCamelCase_						     =					logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)




class      __A(    __lowerCamelCase			):

  """simple docstring"""







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_						):
       os.makedirs(SCREAMING_SNAKE_CASE_	,      exist_ok=SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    {"""source""": """What is love ?""", """target""": """life"""}
       UpperCamelCase__				=    {"""train""": 12, """val""": 2, """test""": 2}
       for split in ["train", "test", "val"]:
            for field in ["source", "target"]:
                 UpperCamelCase__				=    """\n""".join([contents[field]] * n_lines[split]						)
                 with open(os.path.join(SCREAMING_SNAKE_CASE_	,      F"{split}.{field}"						)	,      """w"""						) as f:
                      f.write(SCREAMING_SNAKE_CASE_						)







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_ = "pytorch"						):
       UpperCamelCase__				=    self.get_auto_remove_tmp_dir()
       UpperCamelCase__				=    os.path.join(SCREAMING_SNAKE_CASE_	,      """output"""						)
       UpperCamelCase__				=    os.path.join(SCREAMING_SNAKE_CASE_	,      """data"""						)
       self._create_dummy_data(data_dir=SCREAMING_SNAKE_CASE_						)

       UpperCamelCase__				=    F"\n                --data_dir {data_dir} \\n                --output_dir {output_dir} \\n                --model_name_or_path facebook/rag-sequence-base \\n                --model_type rag_sequence \\n                --do_train \\n                --do_predict \\n                --n_val -1 \\n                --val_check_interval 1.0 \\n                --train_batch_size 2 \\n                --eval_batch_size 1 \\n                --max_source_length 25 \\n                --max_target_length 25 \\n                --val_max_target_length 25 \\n                --test_max_target_length 25 \\n                --label_smoothing 0.1 \\n                --dropout 0.1 \\n                --attention_dropout 0.1 \\n                --weight_decay 0.001 \\n                --adam_epsilon 1e-08 \\n                --max_grad_norm 0.1 \\n                --lr_scheduler polynomial \\n                --learning_rate 3e-04 \\n                --num_train_epochs 1 \\n                --warmup_steps 4 \\n                --gradient_accumulation_steps 1 \\n                --distributed-port 8787 \\n                --use_dummy_dataset 1 \\n                --distributed_retriever {distributed_retriever} \\n            ".split()

       if gpus > 0:
            testargs.append(F"--gpus={gpus}"						)
            if is_apex_available():
                 testargs.append("""--fp16"""						)
       else:
            testargs.append("""--gpus=0"""						)
            testargs.append("""--distributed_backend=ddp_cpu"""						)
            testargs.append("""--num_processes=2"""						)

       UpperCamelCase__				=    [sys.executable, str(Path(finetune_rag.__file__						).resolve()						)] + testargs
       execute_subprocess_async(SCREAMING_SNAKE_CASE_	,      env=self.get_env()						)

       UpperCamelCase__				=    os.path.join(SCREAMING_SNAKE_CASE_	,      """metrics.json"""						)
       with open(SCREAMING_SNAKE_CASE_						) as f:
            UpperCamelCase__				=    json.load(SCREAMING_SNAKE_CASE_						)
       return result







  @require_torch_gpu
  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    self._run_finetune(gpus=1						)
       self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""]	,      0.2						)







  @require_torch_multi_gpu
  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    self._run_finetune(gpus=2						)
       self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""]	,      0.2						)







  @require_torch_gpu
  @require_ray
  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    self._run_finetune(gpus=1	,      distributed_retriever="""ray"""						)
       self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""]	,      0.2						)







  @require_torch_multi_gpu
  @require_ray
  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    self._run_finetune(gpus=1	,      distributed_retriever="""ray"""						)
       self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""]	,      0.2						)







import re

import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey

from ..utils import logging


lowerCamelCase_						     =					logging.get_logger(__name__)






def 						__magic_name__			(   __a      :	Optional[int]						):



     '''simple docstring'''




     UpperCamelCase__				=    R"""\w+[.]\d+"""
     UpperCamelCase__				=    re.findall(__a	,		__a						)
     for pat in pats:
          UpperCamelCase__				=    key.replace(__a	,		"""_""".join(pat.split("""."""						)						)						)
     return key






def 						__magic_name__			(   __a      :	str	,		__a      :	Dict	,		__a      :	int						):



     '''simple docstring'''




     UpperCamelCase__				=    pt_tuple_key[:-1] + ("""scale""",)
     if (
         any("""norm""" in str_ for str_ in pt_tuple_key						)
         and (pt_tuple_key[-1] == "bias")
         and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
         and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
     ):
          UpperCamelCase__				=    pt_tuple_key[:-1] + ("""scale""",)
          return renamed_pt_tuple_key, pt_tensor
     elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
          UpperCamelCase__				=    pt_tuple_key[:-1] + ("""scale""",)
          return renamed_pt_tuple_key, pt_tensor

     # embedding
     if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
          UpperCamelCase__				=    pt_tuple_key[:-1] + ("""embedding""",)
          return renamed_pt_tuple_key, pt_tensor

     # conv layer
     UpperCamelCase__				=    pt_tuple_key[:-1] + ("""kernel""",)
     if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
          UpperCamelCase__				=    pt_tensor.transpose(2	,		3	,		1	,		0						)
          return renamed_pt_tuple_key, pt_tensor

     # linear layer
     UpperCamelCase__				=    pt_tuple_key[:-1] + ("""kernel""",)
     if pt_tuple_key[-1] == "weight":
          UpperCamelCase__				=    pt_tensor.T
          return renamed_pt_tuple_key, pt_tensor

     # old PyTorch layer norm weight
     UpperCamelCase__				=    pt_tuple_key[:-1] + ("""weight""",)
     if pt_tuple_key[-1] == "gamma":
          return renamed_pt_tuple_key, pt_tensor

     # old PyTorch layer norm bias
     UpperCamelCase__				=    pt_tuple_key[:-1] + ("""bias""",)
     if pt_tuple_key[-1] == "beta":
          return renamed_pt_tuple_key, pt_tensor

     return pt_tuple_key, pt_tensor






def 						__magic_name__			(   __a      :	List[Any]	,		__a      :	List[Any]	,		__a      :	Optional[int]=42						):



     '''simple docstring'''




     UpperCamelCase__				=    {k: v.numpy() for k, v in pt_state_dict.items()}

     # Step 2: Since the model is stateless, get random Flax params
     UpperCamelCase__				=    flax_model.init_weights(PRNGKey(__a						)						)

     UpperCamelCase__				=    flatten_dict(__a						)
     UpperCamelCase__				=    {}

     # Need to change some parameters name to match Flax names
     for pt_key, pt_tensor in pt_state_dict.items():
          UpperCamelCase__				=    rename_key(__a						)
          UpperCamelCase__				=    tuple(renamed_pt_key.split("""."""						)						)

          # Correctly rename weight parameters
          UpperCamelCase__		,	UpperCamelCase__				=    rename_key_and_reshape_tensor(__a	,		__a	,		__a						)

          if flax_key in random_flax_state_dict:
               if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
                    raise ValueError(
                        f"PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape "
                        f"{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}."						)

        # also add unexpected weight so that warning is thrown
          UpperCamelCase__				=    jnp.asarray(__a						)

     return unflatten_dict(__a						)





