
"""simple docstring"""







from abc import ABC, abstractmethod
from typing import List, Optional




class  A__       (	_lowerCamelCase):

		def __init__(      self ):
			# test for the above condition
			self.test()

		def 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			:						Optional[int]									=      0
			__lowerCAmelCase			:						List[Any]									=      False
			while not completed:
				if counter == 1:
					self.reset()
				__lowerCAmelCase			:						str									=      self.advance()
				if not self.does_advance(_SCREAMING_SNAKE_CASE ):
					raise Exception(
					    'Custom Constraint is not defined correctly. self.does_advance(self.advance()) must be true.' )

				__lowerCAmelCase			,   __lowerCAmelCase			,   __lowerCAmelCase			:						int									=      self.update(_SCREAMING_SNAKE_CASE )
				counter += 1

				if counter > 1_00_00:
					raise Exception('update() does not fulfill the constraint.' )

			if self.remaining() != 0:
				raise Exception('Custom Constraint is not defined correctly.' )

		@abstractmethod
		def 							__lowerCamelCase							(      self ):
			raise NotImplementedError(
			    f"{self.__class__} is an abstract class. Only classes inheriting this class can be called." )

		@abstractmethod
		def 							__lowerCamelCase							(      self       ,		_SCREAMING_SNAKE_CASE ):
			raise NotImplementedError(
			    f"{self.__class__} is an abstract class. Only classes inheriting this class can be called." )

		@abstractmethod
		def 							__lowerCamelCase							(      self       ,		_SCREAMING_SNAKE_CASE ):
			raise NotImplementedError(
			    f"{self.__class__} is an abstract class. Only classes inheriting this class can be called." )

		@abstractmethod
		def 							__lowerCamelCase							(      self ):
			raise NotImplementedError(
			    f"{self.__class__} is an abstract class. Only classes inheriting this class can be called." )

		@abstractmethod
		def 							__lowerCamelCase							(      self ):
			raise NotImplementedError(
			    f"{self.__class__} is an abstract class. Only classes inheriting this class can be called." )







		@abstractmethod
		def 							__lowerCamelCase							(      self       ,		_SCREAMING_SNAKE_CASE=False ):
			raise NotImplementedError(
			    f"{self.__class__} is an abstract class. Only classes inheriting this class can be called." )




class  A__       (	_lowerCamelCase):

		def __init__(      self       ,		_SCREAMING_SNAKE_CASE ):
			super(_SCREAMING_SNAKE_CASE       ,		self ).__init__()

			if not isinstance(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE ) or len(_SCREAMING_SNAKE_CASE ) == 0:
				raise ValueError(f"`token_ids` has to be a non-empty list, but is {token_ids}." )
			if any((not isinstance(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE ) or token_id < 0) for token_id in token_ids ):
				raise ValueError(f"Each list in `token_ids` has to be a list of positive integers, but is {token_ids}." )

			__lowerCAmelCase			:						List[Any]									=      token_ids

			__lowerCAmelCase			:						List[Any]									=      len(self.token_ids )
			__lowerCAmelCase			:						Optional[Any]									=      -1  # the index of the currently fulfilled step
			__lowerCAmelCase			:						Optional[int]									=      False

		def 							__lowerCamelCase							(      self ):
			if self.completed:
				return None
			return self.token_ids[self.fulfilled_idx + 1]

		def 							__lowerCamelCase							(      self       ,		_SCREAMING_SNAKE_CASE ):
			if not isinstance(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE ):
				raise ValueError(f"`token_id` has to be an `int`, but is {token_id} of type {type(_SCREAMING_SNAKE_CASE )}" )

			if self.completed:
				return False

			return token_id == self.token_ids[self.fulfilled_idx + 1]

		def 							__lowerCamelCase							(      self       ,		_SCREAMING_SNAKE_CASE ):
			if not isinstance(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE ):
				raise ValueError(f"`token_id` has to be an `int`, but is {token_id} of type {type(_SCREAMING_SNAKE_CASE )}" )

			__lowerCAmelCase			:						str									=      False
			__lowerCAmelCase			:						Union[str, Any]									=      False
			__lowerCAmelCase			:						Tuple									=      False

			if self.does_advance(_SCREAMING_SNAKE_CASE ):
				self.fulfilled_idx += 1
				__lowerCAmelCase			:						Tuple									=      True
				if self.fulfilled_idx == (self.seqlen - 1):
					__lowerCAmelCase			:						Dict									=      True
				__lowerCAmelCase			:						Tuple									=      completed
			else:
				# failed to make progress.
				__lowerCAmelCase			:						Any									=      True
				self.reset()
			return stepped, completed, reset

		def 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			:						Union[str, Any]									=      False
			__lowerCAmelCase			:						Tuple									=      0

		def 							__lowerCamelCase							(      self ):
			return self.seqlen - (self.fulfilled_idx + 1)







		def 							__lowerCamelCase							(      self       ,		_SCREAMING_SNAKE_CASE=False ):
			__lowerCAmelCase			:						Optional[Any]									=      PhrasalConstraint(self.token_ids )

			if stateful:
				__lowerCAmelCase			:						int									=      self.seqlen
				__lowerCAmelCase			:						List[Any]									=      self.fulfilled_idx
				__lowerCAmelCase			:						List[Any]									=      self.completed

			return new_constraint




class  A__       :

		def __init__(      self       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE=True ):
			__lowerCAmelCase			:						str									=      max([len(_SCREAMING_SNAKE_CASE ) for one in nested_token_ids] )

			__lowerCAmelCase			:						str									=      {}
			for token_ids in nested_token_ids:
				__lowerCAmelCase			:						List[Any]									=      root
				for tidx, token_id in enumerate(_SCREAMING_SNAKE_CASE ):
					if token_id not in level:
						__lowerCAmelCase			:						Union[str, Any]									=      {}

					__lowerCAmelCase			:						Dict									=      level[token_id]

			if no_subsets and self.has_subsets(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE ):
				raise ValueError(
				    'Each list in `nested_token_ids` can\'t be a complete subset of another list, but is'
				    f" {nested_token_ids}." )

			__lowerCAmelCase			:						List[str]									=      root

		def 							__lowerCamelCase							(      self       ,		_SCREAMING_SNAKE_CASE ):
			__lowerCAmelCase			:						Optional[int]									=      self.trie

			for current_token in current_seq:
				__lowerCAmelCase			:						int									=      start[current_token]

			__lowerCAmelCase			:						Any									=      list(start.keys() )

			return next_tokens

		def 							__lowerCamelCase							(      self       ,		_SCREAMING_SNAKE_CASE ):
			__lowerCAmelCase			:						Tuple									=      self.next_tokens(_SCREAMING_SNAKE_CASE )

			return len(_SCREAMING_SNAKE_CASE ) == 0

		def 							__lowerCamelCase							(      self       ,		_SCREAMING_SNAKE_CASE ):
			__lowerCAmelCase			:						Optional[Any]									=      list(root.values() )
			if len(_SCREAMING_SNAKE_CASE ) == 0:
				return 1
			else:
				return sum([self.count_leaves(_SCREAMING_SNAKE_CASE ) for nn in next_nodes] )







		def 							__lowerCamelCase							(      self       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE ):
			__lowerCAmelCase			:						List[Any]									=      self.count_leaves(_SCREAMING_SNAKE_CASE )
			return len(_SCREAMING_SNAKE_CASE ) != leaf_count




class  A__       (	_lowerCamelCase):

		def __init__(      self       ,		_SCREAMING_SNAKE_CASE ):
			super(_SCREAMING_SNAKE_CASE       ,		self ).__init__()

			if not isinstance(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE ) or len(_SCREAMING_SNAKE_CASE ) == 0:
				raise ValueError(f"`nested_token_ids` has to be a non-empty list, but is {nested_token_ids}." )
			if any(not isinstance(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE ) for token_ids in nested_token_ids ):
				raise ValueError(f"`nested_token_ids` has to be a list of lists, but is {nested_token_ids}." )
			if any(
			    any((not isinstance(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE ) or token_id < 0) for token_id in token_ids )
			    for token_ids in nested_token_ids ):
				raise ValueError(
				    f"Each list in `nested_token_ids` has to be a list of positive integers, but is {nested_token_ids}." )

			__lowerCAmelCase			:						int									=      DisjunctiveTrie(_SCREAMING_SNAKE_CASE )
			__lowerCAmelCase			:						Union[str, Any]									=      nested_token_ids

			__lowerCAmelCase			:						Any									=      self.trie.max_height
			__lowerCAmelCase			:						Union[str, Any]									=      []
			__lowerCAmelCase			:						Tuple									=      False

		def 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			:						Tuple									=      self.trie.next_tokens(self.current_seq )

			if len(_SCREAMING_SNAKE_CASE ) == 0:
				return None
			else:
				return token_list

		def 							__lowerCamelCase							(      self       ,		_SCREAMING_SNAKE_CASE ):
			if not isinstance(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE ):
				raise ValueError(f"`token_id` is supposed to be type `int`, but is {token_id} of type {type(_SCREAMING_SNAKE_CASE )}" )

			__lowerCAmelCase			:						List[str]									=      self.trie.next_tokens(self.current_seq )

			return token_id in next_tokens

		def 							__lowerCamelCase							(      self       ,		_SCREAMING_SNAKE_CASE ):
			if not isinstance(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE ):
				raise ValueError(f"`token_id` is supposed to be type `int`, but is {token_id} of type {type(_SCREAMING_SNAKE_CASE )}" )

			__lowerCAmelCase			:						Tuple									=      False
			__lowerCAmelCase			:						List[str]									=      False
			__lowerCAmelCase			:						List[str]									=      False

			if self.does_advance(_SCREAMING_SNAKE_CASE ):
				self.current_seq.append(_SCREAMING_SNAKE_CASE )
				__lowerCAmelCase			:						str									=      True
			else:
				__lowerCAmelCase			:						Optional[int]									=      True
				self.reset()

			__lowerCAmelCase			:						Dict									=      self.trie.reached_leaf(self.current_seq )
			__lowerCAmelCase			:						Union[str, Any]									=      completed

			return stepped, completed, reset

		def 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			:						List[Any]									=      False
			__lowerCAmelCase			:						List[str]									=      []

		def 							__lowerCamelCase							(      self ):
			if self.completed:
				# since this can be completed without reaching max height
				return 0
			else:
				return self.seqlen - len(self.current_seq )







		def 							__lowerCamelCase							(      self       ,		_SCREAMING_SNAKE_CASE=False ):
			__lowerCAmelCase			:						Any									=      DisjunctiveConstraint(self.token_ids )

			if stateful:
				__lowerCAmelCase			:						Optional[Any]									=      self.seqlen
				__lowerCAmelCase			:						int									=      self.current_seq
				__lowerCAmelCase			:						Union[str, Any]									=      self.completed

			return new_constraint




class  A__       :

		def __init__(      self       ,		_SCREAMING_SNAKE_CASE ):
			__lowerCAmelCase			:						List[str]									=      constraints

			# max # of steps required to fulfill a given constraint
			__lowerCAmelCase			:						Dict									=      max([c.seqlen for c in constraints] )
			__lowerCAmelCase			:						Optional[Any]									=      len(_SCREAMING_SNAKE_CASE )
			__lowerCAmelCase			:						int									=      False

			self.init_state()

		def 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			:						Tuple									=      []
			__lowerCAmelCase			:						Tuple									=      None
			__lowerCAmelCase			:						Any									=      [constraint.copy(stateful=_SCREAMING_SNAKE_CASE ) for constraint in self.constraints]

		def 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			:						Any									=      0
			if self.inprogress_constraint:
				# extra points for having a constraint mid-fulfilled
				add += self.max_seqlen - self.inprogress_constraint.remaining()

			return (len(self.complete_constraints ) * self.max_seqlen) + add

		def 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			:						int									=      []
			if self.inprogress_constraint is None:
				for constraint in self.pending_constraints:  # "pending" == "unfulfilled yet"
					__lowerCAmelCase			:						Union[str, Any]									=      constraint.advance()
					if isinstance(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE ):
						token_list.append(_SCREAMING_SNAKE_CASE )
					elif isinstance(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE ):
						token_list.extend(_SCREAMING_SNAKE_CASE )
			else:
				__lowerCAmelCase			:						int									=      self.inprogress_constraint.advance()
				if isinstance(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE ):
					token_list.append(_SCREAMING_SNAKE_CASE )
				elif isinstance(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE ):
					token_list.extend(_SCREAMING_SNAKE_CASE )

			if len(_SCREAMING_SNAKE_CASE ) == 0:
				return None
			else:
				return token_list

		def 							__lowerCamelCase							(      self       ,		_SCREAMING_SNAKE_CASE ):
			self.init_state()

			if token_ids is not None:
				for token in token_ids:
					# completes or steps **one** constraint
					__lowerCAmelCase			,   __lowerCAmelCase			:						Optional[int]									=      self.add(_SCREAMING_SNAKE_CASE )

					# the entire list of constraints are fulfilled
					if self.completed:
						break

		def 							__lowerCamelCase							(      self       ,		_SCREAMING_SNAKE_CASE ):
			if not isinstance(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE ):
				raise ValueError(f"`token_id` should be an `int`, but is `{token_id}`." )

			__lowerCAmelCase			,   __lowerCAmelCase			:						List[str]									=      False, False

			if self.completed:
				__lowerCAmelCase			:						Dict									=      True
				__lowerCAmelCase			:						Optional[int]									=      False
				return complete, stepped

			if self.inprogress_constraint is not None:
				# In the middle of fulfilling a constraint. If the `token_id` *does* makes an incremental progress to current
				# job, simply update the state

				__lowerCAmelCase			,   __lowerCAmelCase			,   __lowerCAmelCase			:						Tuple									=      self.inprogress_constraint.update(_SCREAMING_SNAKE_CASE )
				if reset:
					# 1. If the next token breaks the progress, then we must restart.
					#     e.g. constraint = "I love pies" and sequence so far is "I love" but `token_id` == "books".

					#     But that doesn't mean we self.init_state(), since we only reset the state for this particular
					#     constraint, not the full list of constraints.

					self.pending_constraints.append(self.inprogress_constraint.copy(stateful=_SCREAMING_SNAKE_CASE ) )
					__lowerCAmelCase			:						List[str]									=      None

				if complete:
					# 2. If the next token completes the constraint, move it to completed list, set
					#     inprogress to None. If there are no pending constraints either, then this full list of constraints
					#     is complete.

					self.complete_constraints.append(self.inprogress_constraint )
					__lowerCAmelCase			:						Dict									=      None

					if len(self.pending_constraints ) == 0:
						# we're done!
						__lowerCAmelCase			:						Optional[int]									=      True

			else:
				# Not in the middle of fulfilling a constraint. So does this `token_id` helps us step towards any of our list
				# of constraints?

				for cidx, pending_constraint in enumerate(self.pending_constraints ):
					if pending_constraint.does_advance(_SCREAMING_SNAKE_CASE ):
						__lowerCAmelCase			,   __lowerCAmelCase			,   __lowerCAmelCase			:						Any									=      pending_constraint.update(_SCREAMING_SNAKE_CASE )

						if not stepped:
							raise Exception(
							    '`constraint.update(token_id)` is not yielding incremental progress, '
							    'even though `constraint.does_advance(token_id)` is true.' )

						if complete:
							self.complete_constraints.append(_SCREAMING_SNAKE_CASE )
							__lowerCAmelCase			:						Optional[Any]									=      None

						if not complete and stepped:
							__lowerCAmelCase			:						Any									=      pending_constraint

						if complete or stepped:
							# If we made any progress at all, then it's at least not a "pending constraint".

							__lowerCAmelCase			:						str									=      (
							    self.pending_constraints[:cidx] + self.pending_constraints[cidx + 1 :]
							)

							if len(self.pending_constraints ) == 0 and self.inprogress_constraint is None:
								# If there's no longer any pending after this and no inprogress either, then we must be
								# complete.

								__lowerCAmelCase			:						Optional[Any]									=      True

							break  # prevent accidentally stepping through multiple constraints with just one token.

			return complete, stepped







		def 							__lowerCamelCase							(      self       ,		_SCREAMING_SNAKE_CASE=True ):
			__lowerCAmelCase			:						Union[str, Any]									=      ConstraintListState(self.constraints )  # we actually never though self.constraints objects
			# throughout this process. So it's at initialization state.

			if stateful:
				__lowerCAmelCase			:						Tuple									=      [
				    constraint.copy(stateful=_SCREAMING_SNAKE_CASE ) for constraint in self.complete_constraints
				]
				if self.inprogress_constraint is not None:
					__lowerCAmelCase			:						List[Any]									=      self.inprogress_constraint.copy(stateful=_SCREAMING_SNAKE_CASE )
				__lowerCAmelCase			:						List[str]									=      [constraint.copy() for constraint in self.pending_constraints]

			return new_state


'''simple docstring'''






import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock

import torch
from accelerate.utils import write_basic_config

from transformers.testing_utils import TestCasePlus, get_gpu_count, run_command, slow, torch_device
from transformers.utils import is_apex_available


logging.basicConfig(level=logging.DEBUG)

lowerCAmelCase__											=	logging.getLogger()


def 		_A     (    ):






 """simple docstring"""



 __lowercase							  = argparse.ArgumentParser()
 parser.add_argument('''-f'''      )
 __lowercase							  = parser.parse_args()
 return args.f


def 		_A     (    A__      ):






 """simple docstring"""



 __lowercase							  = {}
 __lowercase							  = os.path.join(A__     ,				'''all_results.json'''      )
 if os.path.exists(A__      ):
  with open(A__     ,				'''r'''      ) as f:
   __lowercase							  = json.load(A__      )
 else:
  raise ValueError(F"can't find {path}"      )
 return results


def 		_A     (    ):






 """simple docstring"""



 __lowercase							  = torch.cuda.is_available() and torch_device == '''cuda'''
 return is_using_cuda and is_apex_available()


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






class 			lowercase_	(lowerCamelCase__      ):




  """simple docstring"""




  @classmethod
  def 					SCREAMING_SNAKE_CASE      (      cls   :     List[str]			):
   # Write Accelerate config, will pick up on CPU, GPU, and multi-GPU
   __lowercase							  = tempfile.mkdtemp()
   __lowercase							  = os.path.join(cls.tmpdir					,'''default_config.yml'''			)
   write_basic_config(save_location=cls.configPath			)
   __lowercase							  = ['''accelerate''', '''launch''', '''--config_file''', cls.configPath]






  @classmethod
  def 					SCREAMING_SNAKE_CASE      (      cls   :     str			):
   shutil.rmtree(cls.tmpdir			)






  @mock.patch.dict(os.environ					,{'''WANDB_MODE''': '''offline'''}			)
  def 					SCREAMING_SNAKE_CASE      (      self   :     Any			):
   __lowercase							  = self.get_auto_remove_tmp_dir()
   __lowercase							  = F"\n            {self.examples_dir}/pytorch/text-classification/run_glue_no_trainer.py\n            --model_name_or_path distilbert-base-uncased\n            --output_dir {tmp_dir}\n            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv\n            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --learning_rate=1e-4\n            --seed=42\n            --checkpointing_steps epoch\n            --with_tracking\n        ".split()

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

   run_command(self._launch_args + testargs			)
   __lowercase							  = get_results(lowercase__			)
   self.assertGreaterEqual(result['''eval_accuracy''']					,0.7_5			)
   self.assertTrue(os.path.exists(os.path.join(lowercase__					,'''epoch_0'''			)			)			)
   self.assertTrue(os.path.exists(os.path.join(lowercase__					,'''glue_no_trainer'''			)			)			)






  @mock.patch.dict(os.environ					,{'''WANDB_MODE''': '''offline'''}			)
  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[int]			):
   __lowercase							  = self.get_auto_remove_tmp_dir()
   __lowercase							  = F"\n            {self.examples_dir}/pytorch/language-modeling/run_clm_no_trainer.py\n            --model_name_or_path distilgpt2\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --block_size 128\n            --per_device_train_batch_size 5\n            --per_device_eval_batch_size 5\n            --num_train_epochs 2\n            --output_dir {tmp_dir}\n            --checkpointing_steps epoch\n            --with_tracking\n        ".split()

   if torch.cuda.device_count() > 1:
    # Skipping because there are not enough batches to train the model + would need a drop_last to work.
    return

   run_command(self._launch_args + testargs			)
   __lowercase							  = get_results(lowercase__			)
   self.assertLess(result['''perplexity''']					,1_0_0			)
   self.assertTrue(os.path.exists(os.path.join(lowercase__					,'''epoch_0'''			)			)			)
   self.assertTrue(os.path.exists(os.path.join(lowercase__					,'''clm_no_trainer'''			)			)			)






  @mock.patch.dict(os.environ					,{'''WANDB_MODE''': '''offline'''}			)
  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[int]			):
   __lowercase							  = self.get_auto_remove_tmp_dir()
   __lowercase							  = F"\n            {self.examples_dir}/pytorch/language-modeling/run_mlm_no_trainer.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            --output_dir {tmp_dir}\n            --num_train_epochs=1\n            --checkpointing_steps epoch\n            --with_tracking\n        ".split()

   run_command(self._launch_args + testargs			)
   __lowercase							  = get_results(lowercase__			)
   self.assertLess(result['''perplexity''']					,4_2			)
   self.assertTrue(os.path.exists(os.path.join(lowercase__					,'''epoch_0'''			)			)			)
   self.assertTrue(os.path.exists(os.path.join(lowercase__					,'''mlm_no_trainer'''			)			)			)






  @mock.patch.dict(os.environ					,{'''WANDB_MODE''': '''offline'''}			)
  def 					SCREAMING_SNAKE_CASE      (      self   :     Tuple			):
   # with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
   __lowercase							  = 7 if get_gpu_count() > 1 else 2

   __lowercase							  = self.get_auto_remove_tmp_dir()
   __lowercase							  = F"\n            {self.examples_dir}/pytorch/token-classification/run_ner_no_trainer.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            --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            --checkpointing_steps epoch\n            --with_tracking\n        ".split()

   run_command(self._launch_args + testargs			)
   __lowercase							  = get_results(lowercase__			)
   self.assertGreaterEqual(result['''eval_accuracy''']					,0.7_5			)
   self.assertLess(result['''train_loss''']					,0.5			)
   self.assertTrue(os.path.exists(os.path.join(lowercase__					,'''epoch_0'''			)			)			)
   self.assertTrue(os.path.exists(os.path.join(lowercase__					,'''ner_no_trainer'''			)			)			)






  @unittest.skip(reason='''Fix me @muellerzr'''			)
  @mock.patch.dict(os.environ					,{'''WANDB_MODE''': '''offline'''}			)
  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[Any]			):
   __lowercase							  = self.get_auto_remove_tmp_dir()
   __lowercase							  = F"\n            {self.examples_dir}/pytorch/question-answering/run_qa_no_trainer.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            --seed=42\n            --max_train_steps=10\n            --num_warmup_steps=2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --checkpointing_steps epoch\n            --with_tracking\n        ".split()

   run_command(self._launch_args + testargs			)
   __lowercase							  = get_results(lowercase__			)
   # Because we use --version_2_with_negative the testing script uses SQuAD v2 metrics.
   self.assertGreaterEqual(result['''eval_f1''']					,2_8			)
   self.assertGreaterEqual(result['''eval_exact''']					,2_8			)
   self.assertTrue(os.path.exists(os.path.join(lowercase__					,'''epoch_0'''			)			)			)
   self.assertTrue(os.path.exists(os.path.join(lowercase__					,'''qa_no_trainer'''			)			)			)






  @mock.patch.dict(os.environ					,{'''WANDB_MODE''': '''offline'''}			)
  def 					SCREAMING_SNAKE_CASE      (      self   :     Dict			):
   __lowercase							  = self.get_auto_remove_tmp_dir()
   __lowercase							  = F"\n            {self.examples_dir}/pytorch/multiple-choice/run_swag_no_trainer.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            --max_train_steps=20\n            --num_warmup_steps=2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --with_tracking\n        ".split()

   run_command(self._launch_args + testargs			)
   __lowercase							  = get_results(lowercase__			)
   self.assertGreaterEqual(result['''eval_accuracy''']					,0.8			)
   self.assertTrue(os.path.exists(os.path.join(lowercase__					,'''swag_no_trainer'''			)			)			)






  @slow
  @mock.patch.dict(os.environ					,{'''WANDB_MODE''': '''offline'''}			)
  def 					SCREAMING_SNAKE_CASE      (      self   :     List[str]			):
   __lowercase							  = self.get_auto_remove_tmp_dir()
   __lowercase							  = F"\n            {self.examples_dir}/pytorch/summarization/run_summarization_no_trainer.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            --max_train_steps=50\n            --num_warmup_steps=8\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --checkpointing_steps epoch\n            --with_tracking\n        ".split()

   run_command(self._launch_args + testargs			)
   __lowercase							  = get_results(lowercase__			)
   self.assertGreaterEqual(result['''eval_rouge1''']					,1_0			)
   self.assertGreaterEqual(result['''eval_rouge2''']					,2			)
   self.assertGreaterEqual(result['''eval_rougeL''']					,7			)
   self.assertGreaterEqual(result['''eval_rougeLsum''']					,7			)
   self.assertTrue(os.path.exists(os.path.join(lowercase__					,'''epoch_0'''			)			)			)
   self.assertTrue(os.path.exists(os.path.join(lowercase__					,'''summarization_no_trainer'''			)			)			)






  @slow
  @mock.patch.dict(os.environ					,{'''WANDB_MODE''': '''offline'''}			)
  def 					SCREAMING_SNAKE_CASE      (      self   :     List[Any]			):
   __lowercase							  = self.get_auto_remove_tmp_dir()
   __lowercase							  = F"\n            {self.examples_dir}/pytorch/translation/run_translation_no_trainer.py\n            --model_name_or_path sshleifer/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            --max_train_steps=50\n            --num_warmup_steps=8\n            --num_beams=6\n            --learning_rate=3e-3\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            --checkpointing_steps epoch\n            --with_tracking\n        ".split()

   run_command(self._launch_args + testargs			)
   __lowercase							  = get_results(lowercase__			)
   self.assertGreaterEqual(result['''eval_bleu''']					,3_0			)
   self.assertTrue(os.path.exists(os.path.join(lowercase__					,'''epoch_0'''			)			)			)
   self.assertTrue(os.path.exists(os.path.join(lowercase__					,'''translation_no_trainer'''			)			)			)






  @slow
  def 					SCREAMING_SNAKE_CASE      (      self   :     str			):
   __lowercase							  = logging.StreamHandler(sys.stdout			)
   logger.addHandler(lowercase__			)

   __lowercase							  = self.get_auto_remove_tmp_dir()
   __lowercase							  = F"\n            {self.examples_dir}/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py\n            --dataset_name huggingface/semantic-segmentation-test-sample\n            --output_dir {tmp_dir}\n            --max_train_steps=10\n            --num_warmup_steps=2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --checkpointing_steps epoch\n        ".split()

   run_command(self._launch_args + testargs			)
   __lowercase							  = get_results(lowercase__			)
   self.assertGreaterEqual(result['''eval_overall_accuracy''']					,0.1_0			)






  @mock.patch.dict(os.environ					,{'''WANDB_MODE''': '''offline'''}			)
  def 					SCREAMING_SNAKE_CASE      (      self   :     Tuple			):
   __lowercase							  = self.get_auto_remove_tmp_dir()
   __lowercase							  = F"\n            {self.examples_dir}/pytorch/image-classification/run_image_classification_no_trainer.py\n            --model_name_or_path google/vit-base-patch16-224-in21k\n            --dataset_name hf-internal-testing/cats_vs_dogs_sample\n            --learning_rate 1e-4\n            --per_device_train_batch_size 2\n            --per_device_eval_batch_size 1\n            --max_train_steps 2\n            --train_val_split 0.1\n            --seed 42\n            --output_dir {tmp_dir}\n            --with_tracking\n            --checkpointing_steps 1\n        ".split()

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

   run_command(self._launch_args + testargs			)
   __lowercase							  = get_results(lowercase__			)
   # The base model scores a 25%
   self.assertGreaterEqual(result['''eval_accuracy''']					,0.6			)
   self.assertTrue(os.path.exists(os.path.join(lowercase__					,'''step_1'''			)			)			)
   self.assertTrue(os.path.exists(os.path.join(lowercase__					,'''image_classification_no_trainer'''			)			)			)

