



'''simple docstring'''







import argparse
import json
import logging
import os
import sys
from unittest import skip
from unittest.mock import patch

import tensorflow as tf

from transformers.testing_utils import TestCasePlus, get_gpu_count, slow


SCREAMING_SNAKE_CASE__     : Optional[int]				=   [
    os.path.join(os.path.dirname(__file__), dirname)
    for dirname in [
        """text-generation""",
        """text-classification""",
        """token-classification""",
        """language-modeling""",
        """multiple-choice""",
        """question-answering""",
        """summarization""",
        """translation""",
        """image-classification""",
    ]
]
sys.path.extend(SRC_DIRS)


if SRC_DIRS is not None:
							import run_clm
							import run_image_classification
							import run_mlm
							import run_ner
							import run_qa as run_squad
							import run_summarization
							import run_swag
							import run_text_classification
							import run_translation


logging.basicConfig(level=logging.DEBUG)

SCREAMING_SNAKE_CASE__     : Optional[int]				=   logging.getLogger()







def  SCREAMING_SNAKE_CASE__ (				):
		SCREAMING_SNAKE_CASE_						:Any										=      argparse.ArgumentParser()
		parser.add_argument('-f'     )
		SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      parser.parse_args()
		return args.f







def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE     ):
		SCREAMING_SNAKE_CASE_						:List[str]										=      {}
		SCREAMING_SNAKE_CASE_						:int										=      os.path.join(SCREAMING_SNAKE_CASE ,		'all_results.json'     )
		if os.path.exists(SCREAMING_SNAKE_CASE     ):
				with open(SCREAMING_SNAKE_CASE ,		'r'     ) as f:
						SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      json.load(SCREAMING_SNAKE_CASE     )
		else:
				raise ValueError(F'can\'t find {path}'     )
		return results







def  SCREAMING_SNAKE_CASE__ (				):
		return bool(tf.config.list_physical_devices('GPU'     )     )


SCREAMING_SNAKE_CASE__     : Optional[int]				=   logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)





class 				__lowerCAmelCase(							lowerCAmelCase__      ):






			@skip('Skipping until shape inference for to_tf_dataset PR is merged.'	)
			def 				_lowercase	(   self  :							Tuple	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Tuple										=      self.get_auto_remove_tmp_dir()
					SCREAMING_SNAKE_CASE_						:List[str]										=      f'\n            run_text_classification.py\n            --model_name_or_path distilbert-base-uncased\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv\n            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv\n            --do_train\n            --do_eval\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --learning_rate=1e-4\n            --max_steps=10\n            --warmup_steps=2\n            --seed=42\n            --max_seq_length=128\n            '.split()

					if is_cuda_available():
							testargs.append('--fp16'	)

					with patch.object(SCREAMING_SNAKE_CASE      ,       'argv'      ,       SCREAMING_SNAKE_CASE	):
							run_text_classification.main()
							# Reset the mixed precision policy so we don't break other tests
							tf.keras.mixed_precision.set_global_policy('float32'	)
							SCREAMING_SNAKE_CASE_						:Optional[int]										=      get_results(SCREAMING_SNAKE_CASE	)
							self.assertGreaterEqual(result['eval_accuracy']      ,       0.75	)






			def 				_lowercase	(   self  :							str	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:List[str]										=      self.get_auto_remove_tmp_dir()
					SCREAMING_SNAKE_CASE_						:str										=      f'\n            run_clm.py\n            --model_name_or_path distilgpt2\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --do_train\n            --do_eval\n            --block_size 128\n            --per_device_train_batch_size 2\n            --per_device_eval_batch_size 1\n            --num_train_epochs 2\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            '.split()

					if len(tf.config.list_physical_devices('GPU'	)	) > 1:
							# Skipping because there are not enough batches to train the model + would need a drop_last to work.
							return

					with patch.object(SCREAMING_SNAKE_CASE      ,       'argv'      ,       SCREAMING_SNAKE_CASE	):
							run_clm.main()
							SCREAMING_SNAKE_CASE_						:Any										=      get_results(SCREAMING_SNAKE_CASE	)
							self.assertLess(result['eval_perplexity']      ,       100	)






			def 				_lowercase	(   self  :							int	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Optional[int]										=      self.get_auto_remove_tmp_dir()
					SCREAMING_SNAKE_CASE_						:int										=      f'\n            run_mlm.py\n            --model_name_or_path distilroberta-base\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --max_seq_length 64\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --do_train\n            --do_eval\n            --prediction_loss_only\n            --num_train_epochs=1\n            --learning_rate=1e-4\n        '.split()

					with patch.object(SCREAMING_SNAKE_CASE      ,       'argv'      ,       SCREAMING_SNAKE_CASE	):
							run_mlm.main()
							SCREAMING_SNAKE_CASE_						:Optional[Any]										=      get_results(SCREAMING_SNAKE_CASE	)
							self.assertLess(result['eval_perplexity']      ,       42	)






			def 				_lowercase	(   self  :							Optional[Any]	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:List[str]										=      7 if get_gpu_count() > 1 else 2

					SCREAMING_SNAKE_CASE_						:Tuple										=      self.get_auto_remove_tmp_dir()
					SCREAMING_SNAKE_CASE_						:str										=      f'\n            run_ner.py\n            --model_name_or_path bert-base-uncased\n            --train_file tests/fixtures/tests_samples/conll/sample.json\n            --validation_file tests/fixtures/tests_samples/conll/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --do_train\n            --do_eval\n            --warmup_steps=2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=2\n            --num_train_epochs={epochs}\n            --seed 7\n        '.split()

					with patch.object(SCREAMING_SNAKE_CASE      ,       'argv'      ,       SCREAMING_SNAKE_CASE	):
							run_ner.main()
							SCREAMING_SNAKE_CASE_						:Optional[Any]										=      get_results(SCREAMING_SNAKE_CASE	)
							self.assertGreaterEqual(result['accuracy']      ,       0.75	)






			def 				_lowercase	(   self  :							Tuple	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Tuple										=      self.get_auto_remove_tmp_dir()
					SCREAMING_SNAKE_CASE_						:Any										=      f'\n            run_qa.py\n            --model_name_or_path bert-base-uncased\n            --version_2_with_negative\n            --train_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --max_steps=10\n            --warmup_steps=2\n            --do_train\n            --do_eval\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n        '.split()

					with patch.object(SCREAMING_SNAKE_CASE      ,       'argv'      ,       SCREAMING_SNAKE_CASE	):
							run_squad.main()
							SCREAMING_SNAKE_CASE_						:Optional[Any]										=      get_results(SCREAMING_SNAKE_CASE	)
							self.assertGreaterEqual(result['f1']      ,       30	)
							self.assertGreaterEqual(result['exact']      ,       30	)






			def 				_lowercase	(   self  :							Union[str, Any]	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Dict										=      self.get_auto_remove_tmp_dir()
					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      f'\n            run_swag.py\n            --model_name_or_path bert-base-uncased\n            --train_file tests/fixtures/tests_samples/swag/sample.json\n            --validation_file tests/fixtures/tests_samples/swag/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --max_steps=20\n            --warmup_steps=2\n            --do_train\n            --do_eval\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n        '.split()

					with patch.object(SCREAMING_SNAKE_CASE      ,       'argv'      ,       SCREAMING_SNAKE_CASE	):
							run_swag.main()
							SCREAMING_SNAKE_CASE_						:int										=      get_results(SCREAMING_SNAKE_CASE	)
							self.assertGreaterEqual(result['val_accuracy']      ,       0.8	)






			@slow
			def 				_lowercase	(   self  :							List[Any]	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Any										=      self.get_auto_remove_tmp_dir()
					SCREAMING_SNAKE_CASE_						:Optional[int]										=      f'\n            run_summarization.py\n            --model_name_or_path t5-small\n            --train_file tests/fixtures/tests_samples/xsum/sample.json\n            --validation_file tests/fixtures/tests_samples/xsum/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --max_steps=50\n            --warmup_steps=8\n            --do_train\n            --do_eval\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n        '.split()

					with patch.object(SCREAMING_SNAKE_CASE      ,       'argv'      ,       SCREAMING_SNAKE_CASE	):
							run_summarization.main()
							SCREAMING_SNAKE_CASE_						:Dict										=      get_results(SCREAMING_SNAKE_CASE	)
							self.assertGreaterEqual(result['rouge1']      ,       10	)
							self.assertGreaterEqual(result['rouge2']      ,       2	)
							self.assertGreaterEqual(result['rougeL']      ,       7	)
							self.assertGreaterEqual(result['rougeLsum']      ,       7	)






			@slow
			def 				_lowercase	(   self  :							Optional[Any]	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Dict										=      self.get_auto_remove_tmp_dir()
					SCREAMING_SNAKE_CASE_						:List[Any]										=      f'\n            run_translation.py\n            --model_name_or_path Rocketknight1/student_marian_en_ro_6_1\n            --source_lang en\n            --target_lang ro\n            --train_file tests/fixtures/tests_samples/wmt16/sample.json\n            --validation_file tests/fixtures/tests_samples/wmt16/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --warmup_steps=8\n            --do_train\n            --do_eval\n            --learning_rate=3e-3\n            --num_train_epochs 12\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --source_lang en_XX\n            --target_lang ro_RO\n        '.split()

					with patch.object(SCREAMING_SNAKE_CASE      ,       'argv'      ,       SCREAMING_SNAKE_CASE	):
							run_translation.main()
							SCREAMING_SNAKE_CASE_						:str										=      get_results(SCREAMING_SNAKE_CASE	)
							self.assertGreaterEqual(result['bleu']      ,       30	)






			def 				_lowercase	(   self  :							Optional[int]	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:List[str]										=      self.get_auto_remove_tmp_dir()
					SCREAMING_SNAKE_CASE_						:Any										=      f'\n            run_image_classification.py\n            --dataset_name hf-internal-testing/cats_vs_dogs_sample\n            --model_name_or_path microsoft/resnet-18\n            --do_train\n            --do_eval\n            --learning_rate 1e-4\n            --per_device_train_batch_size 2\n            --per_device_eval_batch_size 1\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --dataloader_num_workers 16\n            --num_train_epochs 2\n            --train_val_split 0.1\n            --seed 42\n            --ignore_mismatched_sizes True\n            '.split()

					with patch.object(SCREAMING_SNAKE_CASE      ,       'argv'      ,       SCREAMING_SNAKE_CASE	):
							run_image_classification.main()
							SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      get_results(SCREAMING_SNAKE_CASE	)
							self.assertGreaterEqual(result['accuracy']      ,       0.7	)








from typing import Any

import numpy as np
class 							__snake_case:



 def __init__(						self : str			,		UpperCAmelCase__ : np.ndarray			,		UpperCAmelCase__ : int						):

    """simple docstring"""






    if np.any(tableau[:, -1]			,		where=tableau[:, -1] < 0						):
       raise ValueError("""RHS must be > 0"""						)

    lowercase__    :						List[str]       =			tableau
    lowercase__  ,       lowercase__    :						Optional[Any]       =			tableau.shape

    # Number of decision variables x1, x2, x3...
    lowercase__    :						List[str]       =			n_vars

    # Number of artificial variables to be minimised
    lowercase__    :						str       =			len(np.where(tableau[self.n_vars : -1] == -1						)[0]						)

    # 2 if there are >= or == constraints (nonstandard), 1 otherwise (std)
    lowercase__    :						Optional[Any]       =			(self.n_art_vars > 0) + 1

    # Number of slack variables added to make inequalities into equalities
    lowercase__    :						Optional[int]       =			self.n_rows - self.n_stages

    # Objectives for each stage
    lowercase__    :						Any       =			["""max"""]

    # In two stage simplex, first minimise then maximise
    if self.n_art_vars:
       self.objectives.append("""min"""						)

    lowercase__    :						Union[str, Any]       =			[""""""]

    # Index of current pivot row and column
    lowercase__    :						List[str]       =			None
    lowercase__    :						List[str]       =			None

    # Does objective row only contain (non)-negative values?
    lowercase__    :						str       =			False



 @staticmethod
 def     UpperCAmelCase     (						*UpperCAmelCase__ : int						):

    """simple docstring"""






    if len(UpperCAmelCase__						) != 3:
       raise ValueError("""Must provide n_vars, n_slack, and n_art_vars"""						)

    if not all(x >= 0 and isinstance(UpperCAmelCase__			,		UpperCAmelCase__						) for x in args						):
       raise ValueError("""All arguments must be non-negative integers"""						)

    # decision | slack | artificial
    lowercase__    :						Optional[int]       =			["""x""", """s""", """a"""]
    lowercase__    :						int       =			[]
    for i in range(3						):
       for j in range(args[i]						):
          titles.append(string_starts[i] + str(j + 1						)						)
    titles.append("""RHS"""						)
    return titles



 def     UpperCAmelCase     (						self : List[str]			,		UpperCAmelCase__ : np.ndarray						):

    """simple docstring"""






    lowercase__    :						Optional[Any]       =			self.objectives[-1]

    # Find entries of highest magnitude in objective rows
    lowercase__    :						Union[str, Any]       =			(objective == """min""") - (objective == """max""")
    lowercase__    :						Optional[int]       =			np.argmax(sign * tableau[0, : self.n_vars]						)

    # Choice is only valid if below 0 for maximise, and above for minimise
    if sign * self.tableau[0, col_idx] <= 0:
       lowercase__    :						Optional[int]       =			True
       return 0, 0

    # Pivot row is chosen as having the lowest quotient when elements of
    # the pivot column divide the right-hand side

    # Slice excluding the objective rows
    lowercase__    :						Any       =			slice(self.n_stages			,		self.n_rows						)

    # RHS
    lowercase__    :						Union[str, Any]       =			tableau[s, -1]

    # Elements of pivot column within slice
    lowercase__    :						Optional[int]       =			tableau[s, col_idx]

    # Array filled with nans
    lowercase__    :						Optional[int]       =			np.full(self.n_rows - self.n_stages			,		np.nan						)

    # If element in pivot column is greater than zeron_stages, return
    # quotient or nan otherwise
    lowercase__    :						Optional[int]       =			np.divide(UpperCAmelCase__			,		UpperCAmelCase__			,		out=UpperCAmelCase__			,		where=divisor > 0						)

    # Arg of minimum quotient excluding the nan values. n_stages is added
    # to compensate for earlier exclusion of objective columns
    lowercase__    :						Dict       =			np.nanargmin(UpperCAmelCase__						) + self.n_stages
    return row_idx, col_idx



 def     UpperCAmelCase     (						self : Union[str, Any]			,		UpperCAmelCase__ : np.ndarray			,		UpperCAmelCase__ : int			,		UpperCAmelCase__ : int						):

    """simple docstring"""






    lowercase__    :						int       =			tableau[row_idx].copy()

    lowercase__    :						Optional[Any]       =			piv_row[col_idx]

    # Entry becomes 1
    piv_row *= 1 / piv_val

    # Variable in pivot column becomes basic, ie the only non-zero entry
    for idx, coeff in enumerate(tableau[:, col_idx]						):
       tableau[idx] += -coeff * piv_row
    lowercase__    :						Any       =			piv_row
    return tableau



 def     UpperCAmelCase     (						self : Optional[Any]			,		UpperCAmelCase__ : np.ndarray						):

    """simple docstring"""






    self.objectives.pop()

    if not self.objectives:
       return tableau

    # Slice containing ids for artificial columns
    lowercase__    :						Optional[int]       =			slice(-self.n_art_vars - 1			,		-1						)

    # Delete the artificial variable columns
    lowercase__    :						Dict       =			np.delete(UpperCAmelCase__			,		UpperCAmelCase__			,		axis=1						)

    # Delete the objective row of the first stage
    lowercase__    :						Optional[Any]       =			np.delete(UpperCAmelCase__			,		0			,		axis=0						)

    lowercase__    :						str       =			1
    self.n_rows -= 1
    lowercase__    :						List[str]       =			0
    lowercase__    :						List[Any]       =			False
    return tableau



 def     UpperCAmelCase     (						self : Optional[Any]						):

    """simple docstring"""






    for _ in range(100						):
       # Completion of each stage removes an objective. If both stages
       # are complete, then no objectives are left
       if not self.objectives:
          lowercase__    :						List[Any]       =			self.generate_col_titles(
              self.n_vars			,		self.n_slack			,		self.n_art_vars						)

          # Find the values of each variable at optimal solution
          return self.interpret_tableau(self.tableau			,		self.col_titles						)

       lowercase__  ,       lowercase__    :						Optional[Any]       =			self.find_pivot(self.tableau						)

       # If there are no more negative values in objective row
       if self.stop_iter:
          # Delete artificial variable columns and rows. Update attributes
          lowercase__    :						Any       =			self.change_stage(self.tableau						)
       else:
          lowercase__    :						str       =			self.pivot(self.tableau			,		UpperCAmelCase__			,		UpperCAmelCase__						)
    return {}







 def     UpperCAmelCase     (						self : str			,		UpperCAmelCase__ : np.ndarray			,		UpperCAmelCase__ : list[str]						):

    """simple docstring"""






    lowercase__    :						List[str]       =			{"""P""": abs(tableau[0, -1]						)}

    for i in range(self.n_vars						):
       # Gives ids of nonzero entries in the ith column
       lowercase__    :						int       =			np.nonzero(tableau[:, i]						)
       lowercase__    :						Tuple       =			len(nonzero[0]						)

       # First entry in the nonzero ids
       lowercase__    :						int       =			nonzero[0][0]
       lowercase__    :						Tuple       =			tableau[nonzero_rowidx, i]

       # If there is only one nonzero value in column, which is one
       if n_nonzero == nonzero_val == 1:
          lowercase__    :						Tuple       =			tableau[nonzero_rowidx, -1]
          lowercase__    :						Union[str, Any]       =			rhs_val

        # Check for basic variables
    for title in col_titles:
       # Don't add RHS or slack variables to output dict
       if title[0] not in "R-s-a":
          output_dict.setdefault(UpperCAmelCase__			,		0						)
    return output_dict


if __name__ == "__main__":
 import doctest

 doctest.testmod()


