import itertools
import os
import subprocess
from os.path import dirname

from parameterized import parameterized

from tests.trainer.test_trainer import TrainerIntegrationCommon  # noqa
from transformers import is_torch_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    get_tests_dir,
    require_deepspeed,
    require_torch_gpu,
    slow,
)
from transformers.trainer_utils import set_seed


if is_torch_available():
			from tests.trainer.test_trainer import (  # noqa
			    RegressionModelConfig,
			    RegressionPreTrainedModel,
			    get_regression_trainer,
			)


set_seed(42)

a_					      = get_tests_dir('fixtures')
a_					      = os.path.join(dirname(get_tests_dir()))
a_					      = dirname(os.path.abspath(__file__))

# default torch.distributed port
a_					      = """10999"""

a_					      = """t5-small"""

# *** Working Models ***
a_					      = """hf-internal-testing/tiny-albert"""
a_					      = """sshleifer/bart-tiny-random"""
a_					      = """hf-internal-testing/tiny-bert"""
a_					      = """hf-internal-testing/tiny-random-bigbird_pegasus"""
a_					      = """hf-internal-testing/tiny-random-big_bird"""
a_					      = """hf-internal-testing/tiny-random-blenderbot"""
a_					      = """bigscience/bigscience-small-testing"""
a_					      = """hf-internal-testing/tiny-random-deberta"""
a_					      = """hf-internal-testing/tiny-random-deberta-v2"""
a_					      = """sshleifer/tiny-distilbert-base-cased"""
a_					      = """hf-internal-testing/tiny-electra"""
a_					      = """hf-internal-testing/tiny-random-flaubert"""
a_					      = """stas/tiny-wmt19-en-de"""
a_					      = """hf-internal-testing/tiny-random-funnel"""
a_					      = """sshleifer/tiny-gpt2"""
a_					      = """hf-internal-testing/tiny-random-gptj"""
a_					      = """hf-internal-testing/tiny-random-gpt_neo"""
a_					      = """hf-internal-testing/tiny-layoutlm"""
a_					      = """hf-internal-testing/tiny-random-led"""
a_					      = """hf-internal-testing/tiny-random-longformer"""
a_					      = """stas/tiny-m2m_100"""  # hf tiny model is unsuitable
a_					      = """sshleifer/tiny-marian-en-de"""
a_					      = """sshleifer/tiny-mbart"""
a_					      = """hf-internal-testing/tiny-random-mobilebert"""
a_					      = """hf-internal-testing/tiny-random-mpnet"""
a_					      = """stas/pegasus-cnn_dailymail-tiny-random"""
a_					      = """hf-internal-testing/tiny-random-prophetnet"""
a_					      = """sshleifer/tiny-distilroberta-base"""
a_					      = """hf-internal-testing/tiny-random-squeezebert"""
a_					      = """patrickvonplaten/t5-tiny-random"""
a_					      = """hf-internal-testing/tiny-random-t5-v1.1"""
a_					      = """hf-internal-testing/tiny-random-vit"""
a_					      = """hf-internal-testing/tiny-xlm-roberta"""
a_					      = """sshleifer/tiny-xlnet-base-cased"""


# *** To Fix ***


# *** tiny model issues ***
# missing model files:
a_					      = """hf-internal-testing/tiny-random-mt5"""
a_					      = """hf-internal-testing/tiny-random-camembert"""
a_					      = """hf-internal-testing/tiny-random-openai-gpt"""

# missing tokenizer files
a_					      = """hf-internal-testing/tiny-random-convbert"""
a_					      = """hf-internal-testing/tiny-random-layoutlmv2"""
a_					      = """hf-internal-testing/tiny-random-hubert"""

# issues with tokenizer
a_					      = """hf-internal-testing/tiny-random-ctrl"""
a_					      = """hf-internal-testing/tiny-random-transfo-xl"""  # same as ctrl

# other issues with tiny models
a_					      = """hf-internal-testing/tiny-random-ibert"""  # multiple issues with either mlm/qa/clas
a_					      = """hf-internal-testing/tiny-random-reformer"""  # multiple issues with either mlm/qa/clas

# *** Lacking official examples to test with ***
# or not working with examples
a_					      = """hf-internal-testing/tiny-random-dpr"""
# - "dpr"  examples/research_projects/rag-end2end-retriever/
a_					      = """hf-internal-testing/tiny-random-rag"""
# - "rag" research_projects
a_					      = """"""
# - "luke" Entities classes - no plan to make such example
a_					      = """hf-internal-testing/tiny-random-lxmert"""
# - "lxmert" doesn't work with run_qa.py
a_					      = """hf-internal-testing/tiny-random-clip"""
# - "clip" nothing under pytorch examples - XXX: Suraj is working on adding some - check by end of Sep
a_					      = """hf-internal-testing/tiny-random-speech_to_text"""
# - "speech_to_text", nothing under pytorch examples


# *** Reactive mode ***
# models with low usage, unstable API, things about to change - do nothing about the following until someone runs into a problem
a_					      = """hf-internal-testing/tiny-random-tapas"""





def      __lowercase  (		lowerCamelCase      :  Any=False			):
	UpperCamelCase_      :		int				=      min(2						,						get_gpu_count()			) if distributed else 1
	UpperCamelCase_      :		List[str]				=      os.environ.get('DS_TEST_PORT'						,						__lowerCamelCase			)
	return F"deepspeed --num_nodes 1 --num_gpus {num_gpus} --master_port {master_port}".split()





def      __lowercase  (		):
	UpperCamelCase_      :		str				=      F"{FIXTURE_DIRECTORY}/tests_samples"
	UpperCamelCase_      :		List[str]				=      F"{data_dir_samples}/wmt_en_ro"
	UpperCamelCase_      :		Any				=      F"{data_dir_samples}/xsum"
	UpperCamelCase_      :		str				=      """
        --do_train
        --max_train_samples 4
        --per_device_train_batch_size 2
        --num_train_epochs 1
        --fp16
        --report_to none
        --overwrite_output_dir
        """.split()

	# try to cover as many models as possible once (it's enough to run on one task per model)
	# but need a tiny model for each
	#
	# should have "{model_type.upper()}_TINY" corresponding vars defined, e.g., T5_TINY, etc.
	UpperCamelCase_      :		Tuple				=      {
	    """trans""": [
	        """bart""",
	        """fsmt""",
	        """m2m_100""",
	        """marian""",
	        """mbart""",
	        """t5""",
	        """t5_v1""",
	        # "mt5", missing model files
	    ],
	    """sum""": [
	        """pegasus""",
	    ],
	    """clm""": [
	        """big_bird""",
	        """bigbird_pegasus""",
	        """blenderbot""",
	        """bloom""",
	        """gpt2""",
	        """gpt_neo""",
	        """gptj""",
	        """xlm-roberta""",
	        """prophetnet""",
	        # "camembert", missing model files
	    ],
	    """mlm""": [
	        """albert""",
	        """deberta""",
	        """deberta-v2""",
	        """distilbert""",
	        """electra""",
	        """flaubert""",
	        """funnel""",
	        """layoutlm""",
	        # "reformer", # multiple issues with either mlm/qa/clas
	    ],
	    """qa""": [
	        """led""",
	        """longformer""",
	        """mobilebert""",
	        """mpnet""",
	        """roberta""",
	        """squeezebert""",
	        # "convbert", # missing tokenizer files
	        # "layoutlmv2", missing model files
	    ],
	    """clas""": [
	        """bert""",
	        """xlnet""",
	        # "hubert", # missing tokenizer files
	        # "ibert", # multiple issues with either mlm/qa/clas
	        # "transfo-xl", # tokenizer issues as ctrl
	        # "ctrl", # tokenizer issues
	        # "openai-gpt", missing model files
	        # "tapas", multiple issues
	    ],
	    """img_clas""": [
	        """vit""",
	    ],
	}

	UpperCamelCase_      :		List[Any]				=      F"{ROOT_DIRECTORY}/examples/pytorch"

	UpperCamelCase_      :		str				=      {
	    """trans""": F"\n        {scripts_dir}/translation/run_translation.py\n        --train_file {data_dir_wmt}/train.json\n        --source_lang en\n        --target_lang ro\n        ",
	    """sum""": F"\n        {scripts_dir}/summarization/run_summarization.py\n        --train_file {data_dir_xsum}/sample.json\n        --max_source_length 12\n        --max_target_length 12\n        --lang en\n        ",
	    """clm""": F"\n        {scripts_dir}/language-modeling/run_clm.py\n        --train_file {FIXTURE_DIRECTORY}/sample_text.txt\n        --block_size 8\n        ",
	    """mlm""": F"\n        {scripts_dir}/language-modeling/run_mlm.py\n        --train_file {FIXTURE_DIRECTORY}/sample_text.txt\n        ",
	    """qa""": F"\n        {scripts_dir}/question-answering/run_qa.py\n        --train_file {data_dir_samples}/SQUAD/sample.json\n        ",
	    """clas""": F"\n        {scripts_dir}/text-classification/run_glue.py\n        --train_file {data_dir_samples}/MRPC/train.csv\n        --max_seq_length 12\n        --task_name MRPC\n        ",
	    """img_clas""": F"\n        {scripts_dir}/image-classification/run_image_classification.py\n            --dataset_name hf-internal-testing/cats_vs_dogs_sample\n            --remove_unused_columns False\n            --max_steps 10\n            --image_processor_name {DS_TESTS_DIRECTORY}/vit_feature_extractor.json\n        ",
	}

	UpperCamelCase_      :		Any				=      get_launcher(distributed=__lowerCamelCase			)

	UpperCamelCase_      :		Tuple				=      {}
	for task, args in tasks.items():
		UpperCamelCase_      :		Any				=      args.split()
		for model in tasksamodels[task]:
			UpperCamelCase_      :		List[str]				=      globals()[F"{model.upper().replace('-'						,						'_'			)}_TINY"]
			UpperCamelCase_      :		Union[str, Any]				=      F"--model_name_or_path {model_name}".split()
			UpperCamelCase_      :		Tuple				=      launcher + args + args_model + args_main

			# # generation special case
			# if task == "gen":
			#     launcher = f"deepspeed --num_nodes 1 --num_gpus 1".split()
			#     args_model += f"--model_type {model}".split()
			#     cmds[f"{task}_{model}"] = launcher + args + args_model
			# else:

	return cmds


a_					      = make_task_cmds()

a_					      = """zero2"""
a_					      = """zero3"""

a_					      = [ZEROa, ZEROa]





def      __lowercase  (		lowerCamelCase      :  Optional[int]						,						lowerCamelCase      :  Optional[Any]						,						lowerCamelCase      :  Optional[Any]			):
	UpperCamelCase_      :		Dict				=      parameterized.to_safe_name('_'.join(str(__lowerCamelCase			) for x in param.args			)			)
	return F"{func.__name__}_{param_based_name}"


# Cartesian-product of zero stages with models to test
a_					      = list(itertools.product(stages, task_cmds.keys()))






@slow
@require_deepspeed
@require_torch_gpu
class 	_lowercase		(      __lowerCamelCase ):

	def 		SCREAMING_SNAKE_CASE__     (				self			:							str     ,   snake_case			:							Dict     ,   snake_case			:							Any    )     ->		Dict:

		"""simple docstring"""





		if task not in task_cmds:
			raise ValueError(f"don't know of task {task}, have {task_cmds.keys()}"    )

		UpperCamelCase_      :		Union[str, Any]				=      task_cmds[task]
		UpperCamelCase_      :		List[str]				=      f"--deepspeed {self.test_file_dir_str}/ds_config_{stage}.json".split()

		UpperCamelCase_      :		List[str]				=      self.get_auto_remove_tmp_dir()
		UpperCamelCase_      :		List[str]				=      f"--output_dir {output_dir}".split()

		cmd += args_ds + args_out

		return cmd, output_dir


	@parameterized.expand(_lowerCAmelCase     ,   name_func=_lowerCAmelCase    )
	def 		SCREAMING_SNAKE_CASE__     (				self			:							Optional[Any]     ,   snake_case			:							Dict     ,   snake_case			:							Optional[Any]    )     ->		Union[str, Any]:

		"""simple docstring"""





		UpperCamelCase_      :		Union[str, Any]				=      self.get_task_cmd(_lowerCAmelCase     ,   _lowerCAmelCase    )

		# 1. generate the checkpoint
		cmd += "--save_steps 1".split()
		# keep for quick debug
		# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] + cmd)); die
		execute_subprocess_async(_lowerCAmelCase     ,   env=self.get_env()    )

		# 2. test that the fp32 weights get reconsolidated
		UpperCamelCase_      :		Optional[Any]				=      f"{output_dir}/checkpoint-1"
		UpperCamelCase_      :		Dict				=      f"{chkpt_dir}/out.bin"
		UpperCamelCase_      :		List[str]				=      f"{chkpt_dir}/zero_to_fp32.py {chkpt_dir} {recovered_model_path}"
		# keep for quick debug
		# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
		subprocess.check_call(_lowerCAmelCase     ,   shell=_lowerCAmelCase    )
		assert os.path.exists(_lowerCAmelCase    ), f"{recovered_model_path} was not found"

		# possibly could also test that the resulting saved model is usable but given that we use
		# random models we won't know if it's any good




from typing import Any

import numpy as np





class 	_lowercase		:

 def __init__(				self			:							Dict     ,   snake_case			:							np.ndarray     ,   snake_case			:							int    )     ->		None:

  """simple docstring"""





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

  UpperCamelCase_      :		List[str]				=      tableau
  UpperCamelCase_,				UpperCamelCase_      :		Tuple				=      tableau.shape

  # Number of decision variables x1, x2, x3...
  UpperCamelCase_      :		Dict				=      n_vars

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

  # 2 if there are >= or == constraints (nonstandard), 1 otherwise (std)
  UpperCamelCase_      :		int				=      (self.n_art_vars > 0) + 1

  # Number of slack variables added to make inequalities into equalities
  UpperCamelCase_      :		Union[str, Any]				=      self.n_rows - self.n_stages

  # Objectives for each stage
  UpperCamelCase_      :		Dict				=      ['max']

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

  UpperCamelCase_      :		str				=      ['']

  # Index of current pivot row and column
  UpperCamelCase_      :		str				=      None
  UpperCamelCase_      :		Optional[int]				=      None

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

 @staticmethod
 def 		SCREAMING_SNAKE_CASE__     (				*snake_case			:							int    )     ->		list[str]:

  """simple docstring"""





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

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

  # decision | slack | artificial
  UpperCamelCase_      :		Tuple				=      ['x', 's', 'a']
  UpperCamelCase_      :		Optional[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 		SCREAMING_SNAKE_CASE__     (				self			:							Any     ,   snake_case			:							np.ndarray    )     ->		tuple[Any, Any]:

  """simple docstring"""





  UpperCamelCase_      :		Any				=      self.objectives[-1]

  # Find entries of highest magnitude in objective rows
  UpperCamelCase_      :		str				=      (objective == 'min') - (objective == 'max')
  UpperCamelCase_      :		str				=      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:
   UpperCamelCase_      :		Tuple				=      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
  UpperCamelCase_      :		Dict				=      slice(self.n_stages     ,   self.n_rows    )

  # RHS
  UpperCamelCase_      :		str				=      tableau[s, -1]

  # Elements of pivot column within slice
  UpperCamelCase_      :		List[Any]				=      tableau[s, col_idx]

  # Array filled with nans
  UpperCamelCase_      :		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
  UpperCamelCase_      :		int				=      np.divide(snake_case     ,   snake_case     ,   out=snake_case     ,   where=divisor > 0    )

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

 def 		SCREAMING_SNAKE_CASE__     (				self			:							Any     ,   snake_case			:							np.ndarray     ,   snake_case			:							int     ,   snake_case			:							int    )     ->		np.ndarray:

  """simple docstring"""





  UpperCamelCase_      :		Union[str, Any]				=      tableau[row_idx].copy()

  UpperCamelCase_      :		List[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
  UpperCamelCase_      :		Any				=      piv_row
  return tableau

 def 		SCREAMING_SNAKE_CASE__     (				self			:							str     ,   snake_case			:							np.ndarray    )     ->		np.ndarray:

  """simple docstring"""





  self.objectives.pop()

  if not self.objectives:
   return tableau

  # Slice containing ids for artificial columns
  UpperCamelCase_      :		int				=      slice(-self.n_art_vars - 1     ,   -1    )

  # Delete the artificial variable columns
  UpperCamelCase_      :		Any				=      np.delete(snake_case     ,   snake_case     ,   axis=1    )

  # Delete the objective row of the first stage
  UpperCamelCase_      :		Dict				=      np.delete(snake_case     ,   0     ,   axis=0    )

  UpperCamelCase_      :		List[Any]				=      1
  self.n_rows -= 1
  UpperCamelCase_      :		int				=      0
  UpperCamelCase_      :		Optional[int]				=      False
  return tableau

 def 		SCREAMING_SNAKE_CASE__     (				self			:							Optional[Any]    )     ->		dict[Any, Any]:

  """simple docstring"""





  for _ in range(1_0_0    ):
   # Completion of each stage removes an objective. If both stages
   # are complete, then no objectives are left
   if not self.objectives:
    UpperCamelCase_      :		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    )

   UpperCamelCase_,				UpperCamelCase_      :		Tuple				=      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
    UpperCamelCase_      :		Union[str, Any]				=      self.change_stage(self.tableau    )
   else:
    UpperCamelCase_      :		Union[str, Any]				=      self.pivot(self.tableau     ,   snake_case     ,   snake_case    )
  return {}


 def 		SCREAMING_SNAKE_CASE__     (				self			:							Tuple     ,   snake_case			:							np.ndarray     ,   snake_case			:							list[str]    )     ->		dict[str, float]:

  """simple docstring"""





  UpperCamelCase_      :		str				=      {'P': abs(tableau[0, -1]    )}

  for i in range(self.n_vars    ):
   # Gives ids of nonzero entries in the ith column
   UpperCamelCase_      :		Dict				=      np.nonzero(tableau[:, i]    )
   UpperCamelCase_      :		Union[str, Any]				=      len(nonzero[0]    )

   # First entry in the nonzero ids
   UpperCamelCase_      :		Optional[int]				=      nonzero[0][0]
   UpperCamelCase_      :		Union[str, Any]				=      tableau[nonzero_rowidx, i]

   # If there is only one nonzero value in column, which is one
   if n_nonzero == nonzero_val == 1:
    UpperCamelCase_      :		Optional[int]				=      tableau[nonzero_rowidx, -1]
    UpperCamelCase_      :		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(snake_case     ,   0    )
  return output_dict


if __name__ == "__main__":
   import doctest

   doctest.testmod()



