






"""simple docstring"""


import os
import sys
from pathlib import Path

import pytest

from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node

from .utils import execute_subprocess_async, get_torch_dist_unique_port, require_torch



def  lowerCAmelCase    (   ):




  """simple docstring"""







  __A						      =			Dataset.from_dict({'''i''': range(1_7			)}			)
  __A						      =			len(__UpperCamelCase			)
  __A						      =			3
  __A						      =			[
      split_dataset_by_node(__UpperCamelCase		,	rank=__UpperCamelCase		,	world_size=__UpperCamelCase			) for rank in range(__UpperCamelCase			)
  ]
  assert sum(len(__UpperCamelCase			) for ds in datasets_per_rank			) == full_size
  assert len({tuple(x.values()			) for ds in datasets_per_rank for x in ds}			) == full_size



def  lowerCAmelCase    (   ):




  """simple docstring"""







  def gen():
    return ({"i": i} for i in range(1_7			))

  __A						      =			3
  __A						      =			IterableDataset.from_generator(__UpperCamelCase			)
  __A						      =			len(list(__UpperCamelCase			)			)
  __A						      =			[
      split_dataset_by_node(__UpperCamelCase		,	rank=__UpperCamelCase		,	world_size=__UpperCamelCase			) for rank in range(__UpperCamelCase			)
  ]
  assert sum(len(list(__UpperCamelCase			)			) for ds in datasets_per_rank			) == full_size
  assert len({tuple(x.values()			) for ds in datasets_per_rank for x in ds}			) == full_size



@pytest.mark.parametrize('''shards_per_node'''		,	[1, 2, 3]			)
def  lowerCAmelCase    (   __UpperCamelCase			):




  """simple docstring"""







  def gen(__UpperCamelCase			):
    for shard in shards:
      yield from ({"i": i, "shard": shard} for i in range(1_7			))

  __A						      =			3
  __A						      =			shards_per_node * world_size
  __A						      =			{'''shards''': [f'shard_{shard_idx}.txt' for shard_idx in range(__UpperCamelCase			)]}
  __A						      =			IterableDataset.from_generator(__UpperCamelCase		,	gen_kwargs=__UpperCamelCase			)
  __A						      =			len(list(__UpperCamelCase			)			)
  assert full_ds.n_shards == world_size * shards_per_node
  __A						      =			[
      split_dataset_by_node(__UpperCamelCase		,	rank=__UpperCamelCase		,	world_size=__UpperCamelCase			) for rank in range(__UpperCamelCase			)
  ]
  assert [ds.n_shards for ds in datasets_per_rank] == [shards_per_node] * world_size
  assert sum(len(list(__UpperCamelCase			)			) for ds in datasets_per_rank			) == full_size
  assert len({tuple(x.values()			) for ds in datasets_per_rank for x in ds}			) == full_size



def  lowerCAmelCase    (   ):




  """simple docstring"""







  def gen():
    return ({"i": i} for i in range(1_7			))

  __A						      =			2
  __A						      =			IterableDataset.from_generator(__UpperCamelCase			)
  __A						      =			len(list(__UpperCamelCase			)			)

  __A						      =			split_dataset_by_node(__UpperCamelCase		,	rank=0		,	world_size=__UpperCamelCase			).shuffle(seed=4_2			)
  assert len(list(__UpperCamelCase			)			) == 1 + full_size // world_size
  with pytest.raises(__UpperCamelCase			):
    split_dataset_by_node(__UpperCamelCase		,	rank=0		,	world_size=__UpperCamelCase			).shuffle()

  __A						      =			split_dataset_by_node(full_ds.shuffle(seed=4_2			)		,	rank=0		,	world_size=__UpperCamelCase			)
  assert len(list(__UpperCamelCase			)			) == 1 + full_size // world_size
  with pytest.raises(__UpperCamelCase			):
    split_dataset_by_node(full_ds.shuffle()		,	rank=0		,	world_size=__UpperCamelCase			)



@pytest.mark.parametrize('''streaming'''		,	[False, True]			)
@require_torch
@pytest.mark.skipif(os.name == '''nt'''		,	reason='''execute_subprocess_async doesn\'t support windows'''			)
@pytest.mark.integration
def  lowerCAmelCase    (   __UpperCamelCase			):




  """simple docstring"""







  __A						      =			2
  __A						      =			get_torch_dist_unique_port()
  __A						      =			Path(__UpperCamelCase			).resolve().parent / '''distributed_scripts''' / '''launch_torch_distributed.py'''
  __A						      =			f'\n        -m torch.distributed.launch\n        --nproc_per_node={nproc_per_node}\n        --master_port={master_port}\n        {test_script}\n    '.split()
  __A						      =			f'\n        --streaming={streaming}\n    '.split()
  __A						      =			[sys.executable] + distributed_args + args
  execute_subprocess_async(__UpperCamelCase		,	env=os.environ.copy()			)





@pytest.mark.parametrize(
    '''nproc_per_node, num_workers'''		,	[
        (2, 2),  # each node has 2 shards and each worker has 1 shards
        (3, 2),  # each node uses all the shards but skips examples, and each worker has 2 shards
    ]		,	)
@require_torch
@pytest.mark.skipif(os.name == '''nt'''		,	reason='''execute_subprocess_async doesn\'t support windows'''			)
@pytest.mark.integration
def  lowerCAmelCase    (   __UpperCamelCase		,	__UpperCamelCase			):




  """simple docstring"""







  __A						      =			True
  __A						      =			get_torch_dist_unique_port()
  __A						      =			Path(__UpperCamelCase			).resolve().parent / '''distributed_scripts''' / '''launch_torch_distributed.py'''
  __A						      =			f'\n        -m torch.distributed.launch\n        --nproc_per_node={nproc_per_node}\n        --master_port={master_port}\n        {test_script}\n    '.split()
  __A						      =			f'\n        --streaming={streaming}\n        --num_workers={num_workers}\n    '.split()
  __A						      =			[sys.executable] + distributed_args + args
  execute_subprocess_async(__UpperCamelCase		,	env=os.environ.copy()			)












"""simple docstring"""


from __future__ import annotations

import unittest

from transformers import CTRLConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
       import tensorflow as tf

       from transformers.models.ctrl.modeling_tf_ctrl import (
           TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
           TFCTRLForSequenceClassification,
           TFCTRLLMHeadModel,
           TFCTRLModel,
       )


class  snake_case		(							_lowerCAmelCase						):


   '''simple docstring'''

   def __init__(     self			:					Optional[int],	_lowerCamelCase			:					Any,	):

     '''simple docstring'''
     __A						      =			parent
     __A						      =			13
     __A						      =			7
     __A						      =			True
     __A						      =			True
     __A						      =			True
     __A						      =			True
     __A						      =			True
     __A						      =			99
     __A						      =			32
     __A						      =			2
     __A						      =			4
     __A						      =			37
     __A						      =			'''gelu'''
     __A						      =			0.1
     __A						      =			0.1
     __A						      =			5_12
     __A						      =			16
     __A						      =			2
     __A						      =			0.02
     __A						      =			3
     __A						      =			4
     __A						      =			None
     __A						      =			self.vocab_size - 1


   def     _SCREAMING_SNAKE_CASE    (     self			:					str  ):

     '''simple docstring'''
     __A						      =			ids_tensor([self.batch_size, self.seq_length],	self.vocab_size  )

     __A						      =			None
     if self.use_input_mask:
       __A						      =			random_attention_mask([self.batch_size, self.seq_length]  )

     __A						      =			None
     if self.use_token_type_ids:
       __A						      =			ids_tensor([self.batch_size, self.seq_length],	self.type_vocab_size  )

     __A						      =			None
     if self.use_mc_token_ids:
       __A						      =			ids_tensor([self.batch_size, self.num_choices],	self.seq_length  )

     __A						      =			None
     __A						      =			None
     __A						      =			None
     if self.use_labels:
       __A						      =			ids_tensor([self.batch_size],	self.type_sequence_label_size  )
       __A						      =			ids_tensor([self.batch_size, self.seq_length],	self.num_labels  )
       __A						      =			ids_tensor([self.batch_size],	self.num_choices  )

     __A						      =			CTRLConfig(
         vocab_size=self.vocab_size,	n_embd=self.hidden_size,	n_layer=self.num_hidden_layers,	n_head=self.num_attention_heads,	n_positions=self.max_position_embeddings,	pad_token_id=self.pad_token_id,	)

     __A						      =			ids_tensor([self.num_hidden_layers, self.num_attention_heads],	2  )

     return (
         config,
         input_ids,
         input_mask,
         head_mask,
         token_type_ids,
         mc_token_ids,
         sequence_labels,
         token_labels,
         choice_labels,
     )


   def     _SCREAMING_SNAKE_CASE    (     self			:					Optional[Any],	_lowerCamelCase			:					Optional[int],	_lowerCamelCase			:					Dict,	_lowerCamelCase			:					Union[str, Any],	_lowerCamelCase			:					int,	_lowerCamelCase			:					List[Any],	*_lowerCamelCase			:					List[Any]  ):

     '''simple docstring'''
     __A						      =			TFCTRLModel(config=_lowerCamelCase  )
     __A						      =			{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
     __A						      =			model(_lowerCamelCase  )

     __A						      =			[input_ids, None, input_mask]  # None is the input for 'past'
     __A						      =			model(_lowerCamelCase  )

     __A						      =			model(_lowerCamelCase  )

     self.parent.assertEqual(result.last_hidden_state.shape,	(self.batch_size, self.seq_length, self.hidden_size)  )


   def     _SCREAMING_SNAKE_CASE    (     self			:					int,	_lowerCamelCase			:					Dict,	_lowerCamelCase			:					Optional[Any],	_lowerCamelCase			:					Union[str, Any],	_lowerCamelCase			:					Any,	_lowerCamelCase			:					Optional[Any],	*_lowerCamelCase			:					Tuple  ):

     '''simple docstring'''
     __A						      =			TFCTRLLMHeadModel(config=_lowerCamelCase  )
     __A						      =			{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
     __A						      =			model(_lowerCamelCase  )
     self.parent.assertEqual(result.logits.shape,	(self.batch_size, self.seq_length, self.vocab_size)  )


   def     _SCREAMING_SNAKE_CASE    (     self			:					Optional[int],	_lowerCamelCase			:					Optional[Any],	_lowerCamelCase			:					Dict,	_lowerCamelCase			:					Tuple,	_lowerCamelCase			:					Dict,	_lowerCamelCase			:					List[str],	*_lowerCamelCase			:					List[str]  ):

     '''simple docstring'''
     __A						      =			self.num_labels
     __A						      =			ids_tensor([self.batch_size],	self.type_sequence_label_size  )
     __A						      =			{
         '''input_ids''': input_ids,
         '''token_type_ids''': token_type_ids,
         '''labels''': sequence_labels,
     }
     __A						      =			TFCTRLForSequenceClassification(_lowerCamelCase  )
     __A						      =			model(_lowerCamelCase  )
     self.parent.assertEqual(result.logits.shape,	(self.batch_size, self.num_labels)  )






   def     _SCREAMING_SNAKE_CASE    (     self			:					Tuple  ):

     '''simple docstring'''
     __A						      =			self.prepare_config_and_inputs()

     (
         (
         __A
     )      ,   (
         __A
     )      ,   (
         __A
     )      ,   (
         __A
     )      ,   (
         __A
     )      ,   (
         __A
     )      ,   (
         __A
     )      ,   (
         __A
     )      ,   (
         __A
     )      ,   
     )						      =			config_and_inputs

     __A						      =			{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
     return config, inputs_dict





@require_tf
class  snake_case		(							_lowerCAmelCase       ,     _lowerCAmelCase       ,     unittest.TestCase						):


   '''simple docstring'''

   A_						:     Optional[int]									=			(TFCTRLModel, TFCTRLLMHeadModel, TFCTRLForSequenceClassification) if is_tf_available() else ()
   A_						:     str									=			(TFCTRLLMHeadModel,) if is_tf_available() else ()
   A_						:     int									=			(
       {
           "feature-extraction": TFCTRLModel,
           "text-classification": TFCTRLForSequenceClassification,
           "text-generation": TFCTRLLMHeadModel,
           "zero-shot": TFCTRLForSequenceClassification,
       }
       if is_tf_available()
       else {}
   )
   A_						:     Union[str, Any]									=			False
   A_						:     Dict									=			False


   def     _SCREAMING_SNAKE_CASE    (     self			:					Optional[int],	_lowerCamelCase			:					Union[str, Any],	_lowerCamelCase			:					int,	_lowerCamelCase			:					List[str],	_lowerCamelCase			:					str,	_lowerCamelCase			:					str  ):

     '''simple docstring'''
     if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
       # Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
       # `CTRLConfig` was never used in pipeline tests, either because of a missing checkpoint or because a tiny
       # config could not be created.
       return True

     return False


   def     _SCREAMING_SNAKE_CASE    (     self			:					Tuple  ):

     '''simple docstring'''
     __A						      =			TFCTRLModelTester(self  )
     __A						      =			ConfigTester(self,	config_class=_lowerCamelCase,	n_embd=37  )


   def     _SCREAMING_SNAKE_CASE    (     self			:					List[str]  ):

     '''simple docstring'''
     self.config_tester.run_common_tests()


   def     _SCREAMING_SNAKE_CASE    (     self			:					List[Any]  ):

     '''simple docstring'''
     __A						      =			self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_ctrl_model(*_lowerCamelCase  )


   def     _SCREAMING_SNAKE_CASE    (     self			:					List[str]  ):

     '''simple docstring'''
     __A						      =			self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_ctrl_lm_head(*_lowerCamelCase  )


   def     _SCREAMING_SNAKE_CASE    (     self			:					Optional[Any]  ):

     '''simple docstring'''
     __A						      =			self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_ctrl_for_sequence_classification(*_lowerCamelCase  )


   def     _SCREAMING_SNAKE_CASE    (     self			:					str  ):

     '''simple docstring'''
     __A      ,   __A						      =			self.model_tester.prepare_config_and_inputs_for_common()
     __A						      =			[TFCTRLLMHeadModel]
     __A						      =			[TFCTRLForSequenceClassification]

     for model_class in self.all_model_classes:
       __A						      =			model_class(_lowerCamelCase  )
       model.build()  # may be needed for the get_bias() call below
       assert isinstance(model.get_input_embeddings(),	tf.keras.layers.Layer  )

       if model_class in list_lm_models:
         __A						      =			model.get_output_embeddings()
         assert isinstance(_lowerCamelCase,	tf.keras.layers.Layer  )
         __A						      =			model.get_bias()
         assert isinstance(_lowerCamelCase,	_lowerCamelCase  )
         for k, v in name.items():
           assert isinstance(_lowerCamelCase,	tf.Variable  )
       elif model_class in list_other_models_with_output_ebd:
         __A						      =			model.get_output_embeddings()
         assert isinstance(_lowerCamelCase,	tf.keras.layers.Layer  )
         __A						      =			model.get_bias()
         assert name is None
       else:
         __A						      =			model.get_output_embeddings()
         assert x is None
         __A						      =			model.get_bias()
         assert name is None






   @slow
   def     _SCREAMING_SNAKE_CASE    (     self			:					str  ):

     '''simple docstring'''
     for model_name in TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       __A						      =			TFCTRLModel.from_pretrained(_lowerCamelCase  )
       self.assertIsNotNone(_lowerCamelCase  )





@require_tf
class  snake_case		(							unittest.TestCase						):


   '''simple docstring'''

   @slow
   def     _SCREAMING_SNAKE_CASE    (     self			:					Dict  ):

     '''simple docstring'''
     __A						      =			TFCTRLLMHeadModel.from_pretrained('''ctrl'''  )
     __A						      =			tf.convert_to_tensor([[1_18_59, 0, 16_11, 8]],	dtype=tf.intaa  )  # Legal the president is
     __A						      =			[
         1_18_59,
         0,
         16_11,
         8,
         5,
         1_50,
         2_64_49,
         2,
         19,
         3_48,
         4_69,
         3,
         25_95,
         48,
         2_07_40,
         24_65_33,
         24_65_33,
         19,
         30,
         5,
     ]  # Legal the president is a good guy and I don't want to lose my job. \n \n I have a

     __A						      =			model.generate(_lowerCamelCase,	do_sample=_lowerCamelCase  )
     self.assertListEqual(output_ids[0].numpy().tolist(),	_lowerCamelCase  )




