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)
UpperCamelCase__											=					logging.getLogger()

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





class      A							(				UpperCAmelCase_     ):


   def 			lowercase_      (self     :						str       ,  __UpperCAmelCase     :						int		)						->				str:
         """simple docstring"""



         os.makedirs(__UpperCAmelCase       ,  exist_ok=__UpperCAmelCase		)
         UpperCAmelCase__								=  {"source": "What is love ?", "target": "life"}
         UpperCAmelCase__								=  {"train": 1_2, "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(__UpperCAmelCase       ,  f"""{split}.{field}"""		)       ,  "w"		) as f:
                           f.write(__UpperCAmelCase		)


   def 			lowercase_      (self     :						Optional[int]       ,  __UpperCAmelCase     :						int       ,  __UpperCAmelCase     :						str = "pytorch"		)						->				Optional[int]:
         """simple docstring"""



         UpperCAmelCase__								=  self.get_auto_remove_tmp_dir()
         UpperCAmelCase__								=  os.path.join(__UpperCAmelCase       ,  "output"		)
         UpperCAmelCase__								=  os.path.join(__UpperCAmelCase       ,  "data"		)
         self._create_dummy_data(data_dir=__UpperCAmelCase		)

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

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


   @require_torch_gpu
   def 			lowercase_      (self     :						Optional[int]		)						->				Tuple:
         """simple docstring"""



         UpperCAmelCase__								=  self._run_finetune(gpus=1		)
         self.assertGreaterEqual(result["test"][0]["test_avg_em"]       ,  0.2		)


   @require_torch_multi_gpu
   def 			lowercase_      (self     :						List[str]		)						->				Tuple:
         """simple docstring"""



         UpperCAmelCase__								=  self._run_finetune(gpus=2		)
         self.assertGreaterEqual(result["test"][0]["test_avg_em"]       ,  0.2		)


   @require_torch_gpu
   @require_ray
   def 			lowercase_      (self     :						Dict		)						->				Optional[int]:
         """simple docstring"""



         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 			lowercase_      (self     :						Optional[Any]		)						->				Tuple:
         """simple docstring"""



         UpperCAmelCase__								=  self._run_finetune(gpus=1       ,  distributed_retriever="ray"		)
         self.assertGreaterEqual(result["test"][0]["test_avg_em"]       ,  0.2		)




from __future__ import annotations

from scipy.special import comb  # type: ignore





class      A							:


   def __init__(self     :						List[Any]       ,  __UpperCAmelCase     :						list[tuple[float, float]]		)						->				List[str]:
         """simple docstring"""



         UpperCAmelCase__								=  list_of_points
         # Degree determines the flexibility of the curve.
         # Degree = 1 will produce a straight line.
         UpperCAmelCase__								=  len(__UpperCAmelCase		) - 1


   def 			lowercase_      (self     :						int       ,  __UpperCAmelCase     :						float		)						->				list[float]:
         """simple docstring"""



         assert 0 <= t <= 1, "Time t must be between 0 and 1."
         UpperCAmelCase__								=  []
         for i in range(len(self.list_of_points		)		):
               # basis function for each i
               output_values.append(
                   comb(self.degree       ,  __UpperCAmelCase		) * ((1 - t) ** (self.degree - i)) * (t**i)		)
         # the basis must sum up to 1 for it to produce a valid Bezier curve.
         assert round(sum(__UpperCAmelCase		)       ,  5		) == 1
         return output_values


   def 			lowercase_      (self     :						Dict       ,  __UpperCAmelCase     :						float		)						->				tuple[float, float]:
         """simple docstring"""



         assert 0 <= t <= 1, "Time t must be between 0 and 1."

         UpperCAmelCase__								=  self.basis_function(__UpperCAmelCase		)
         UpperCAmelCase__								=  0.0
         UpperCAmelCase__								=  0.0
         for i in range(len(self.list_of_points		)		):
               # For all points, sum up the product of i-th basis function and i-th point.
               x += basis_function[i] * self.list_of_points[i][0]
               y += basis_function[i] * self.list_of_points[i][1]
         return (x, y)





   def 			lowercase_      (self     :						Optional[int]       ,  __UpperCAmelCase     :						float = 0.01		)						->				Optional[int]:
         """simple docstring"""



         from matplotlib import pyplot as plt  # type: ignore

         UpperCAmelCase__								=  []  # x coordinates of points to plot
         UpperCAmelCase__								=  []  # y coordinates of points to plot

         UpperCAmelCase__								=  0.0
         while t <= 1:
               UpperCAmelCase__								=  self.bezier_curve_function(__UpperCAmelCase		)
               to_plot_x.append(value[0]		)
               to_plot_y.append(value[1]		)
               t += step_size

         UpperCAmelCase__								=  [i[0] for i in self.list_of_points]
         UpperCAmelCase__								=  [i[1] for i in self.list_of_points]

         plt.plot(
             __UpperCAmelCase       ,  __UpperCAmelCase       ,  color="blue"       ,  label="Curve of Degree " + str(self.degree		)       ,  )
         plt.scatter(__UpperCAmelCase       ,  __UpperCAmelCase       ,  color="red"       ,  label="Control Points"		)
         plt.legend()
         plt.show()


if __name__ == "__main__":
      import doctest

      doctest.testmod()

      BezierCurve([(1, 2), (3, 5)]).plot_curve()  # degree 1
      BezierCurve([(0, 0), (5, 5), (5, 0)]).plot_curve()  # degree 2
      BezierCurve([(0, 0), (5, 5), (5, 0), (2.5, -2.5)]).plot_curve()  # degree 3



