

from __future__ import annotations

import copy
import os
import tempfile
from importlib import import_module
from math import isnan

from transformers import is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import _tf_gpu_memory_limit, require_tf, slow

from ..test_modeling_tf_common import ids_tensor


if is_tf_available():
      import numpy as np
      import tensorflow as tf

      from transformers import (
          TF_MODEL_FOR_CAUSAL_LM_MAPPING,
          TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
          TF_MODEL_FOR_MASKED_LM_MAPPING,
          TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
          TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
          TF_MODEL_FOR_PRETRAINING_MAPPING,
          TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
          TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
          TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
          TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
          TFSharedEmbeddings,
      )

      if _tf_gpu_memory_limit is not None:
            _SCREAMING_SNAKE_CASE							 =			tf.config.list_physical_devices('''GPU''')
            for gpu in gpus:
                  # Restrict TensorFlow to only allocate x GB of memory on the GPUs
                  try:
                        tf.config.set_logical_device_configuration(
                            gpu, [tf.config.LogicalDeviceConfiguration(memory_limit=_tf_gpu_memory_limit)]
                        )
                        _SCREAMING_SNAKE_CASE							 =			tf.config.list_logical_devices('''GPU''')
                        print('''Logical GPUs''', logical_gpus)
                  except RuntimeError as e:
                        # Virtual devices must be set before GPUs have been initialized
                        print(e)


@require_tf
class      lowerCamelCase_ :
 _lowerCAmelCase									=	None
 _lowerCAmelCase									=	()
 _lowerCAmelCase									=	()
 _lowerCAmelCase									=	True
 _lowerCAmelCase									=	True
 _lowerCAmelCase									=	True
 _lowerCAmelCase									=	False




 def     a       (			self  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase=False							):
   __UpperCAmelCase	:  str		=     copy.deepcopy(_UpperCAmelCase							)

   if model_class in get_values(_UpperCAmelCase							):
     __UpperCAmelCase	:  List[str]		=     {
         k: tf.tile(tf.expand_dims(_UpperCAmelCase  ,  1							)  ,  (1, self.model_tester.num_choices) + (1,) * (v.ndim - 1)							)
         if isinstance(_UpperCAmelCase  ,  tf.Tensor							) and v.ndim > 0
         else v
         for k, v in inputs_dict.items()
     }

   if return_labels:
     if model_class in get_values(_UpperCAmelCase							):
       __UpperCAmelCase	:  List[Any]		=     tf.ones(self.model_tester.batch_size  ,  dtype=tf.intaa							)
     elif model_class in get_values(_UpperCAmelCase							):
       __UpperCAmelCase	:  str		=     tf.zeros(self.model_tester.batch_size  ,  dtype=tf.intaa							)
       __UpperCAmelCase	:  Optional[Any]		=     tf.zeros(self.model_tester.batch_size  ,  dtype=tf.intaa							)
     elif model_class in [
         *get_values(_UpperCAmelCase							),
         *get_values(_UpperCAmelCase							),
     ]:
       __UpperCAmelCase	:  Union[str, Any]		=     tf.zeros(self.model_tester.batch_size  ,  dtype=tf.intaa							)
     elif model_class in get_values(_UpperCAmelCase							):
       __UpperCAmelCase	:  int		=     tf.zeros(self.model_tester.batch_size  ,  dtype=tf.intaa							)
     elif model_class in [
         *get_values(_UpperCAmelCase							),
         *get_values(_UpperCAmelCase							),
         *get_values(_UpperCAmelCase							),
         *get_values(_UpperCAmelCase							),
         *get_values(_UpperCAmelCase							),
     ]:
       __UpperCAmelCase	:  List[Any]		=     tf.zeros(
           (self.model_tester.batch_size, self.model_tester.seq_length)  ,  dtype=tf.intaa							)
   return inputs_dict




 @slow
 def     a       (			self							):
   __UpperCAmelCase,			__UpperCAmelCase	:  Dict		=     self.model_tester.prepare_config_and_inputs_for_common()
   for model_class in self.all_model_classes[:2]:
     __UpperCAmelCase	:  int		=     self._prepare_for_class(_UpperCAmelCase  ,  _UpperCAmelCase							)
     __UpperCAmelCase	:  int		=     model_class(_UpperCAmelCase							)

     @tf.function
     def run_in_graph_mode():
       return model(_UpperCAmelCase							)

     __UpperCAmelCase	:  Optional[Any]		=     run_in_graph_mode()
     self.assertIsNotNone(_UpperCAmelCase							)




 @slow
 def     a       (			self							):
   __UpperCAmelCase,			__UpperCAmelCase	:  List[Any]		=     self.model_tester.prepare_config_and_inputs_for_common()
   for model_class in self.all_model_classes[:2]:
     __UpperCAmelCase	:  List[str]		=     self._prepare_for_class(_UpperCAmelCase  ,  _UpperCAmelCase							)
     __UpperCAmelCase	:  Optional[Any]		=     model_class(_UpperCAmelCase							)

     @tf.function(experimental_compile=_UpperCAmelCase							)
     def run_in_graph_mode():
       return model(_UpperCAmelCase							)

     __UpperCAmelCase	:  List[Any]		=     run_in_graph_mode()
     self.assertIsNotNone(_UpperCAmelCase							)




 @slow
 def     a       (			self							):
   # This is a copy of the test_keras_fit method, but we use XLA compilation instead of eager
   __UpperCAmelCase,			__UpperCAmelCase	:  Optional[Any]		=     self.model_tester.prepare_config_and_inputs_for_common()
   for model_class in self.all_model_classes[:2]:
     __UpperCAmelCase	:  List[Any]		=     model_class(_UpperCAmelCase							)
     if getattr(_UpperCAmelCase  ,  'hf_compute_loss'  ,  _UpperCAmelCase							):
       # Test that model correctly compute the loss with kwargs
       __UpperCAmelCase	:  int		=     self._prepare_for_class(inputs_dict.copy()  ,  _UpperCAmelCase  ,  return_labels=_UpperCAmelCase							)
       # Is there a better way to remove these decoder inputs?
       __UpperCAmelCase	:  Union[str, Any]		=     {
           key: val
           for key, val in prepared_for_class.items()
           if key not in ('head_mask', 'decoder_head_mask', 'cross_attn_head_mask', 'decoder_input_ids')
       }

       __UpperCAmelCase	:  Optional[Any]		=     {
           'labels',
           'label',
           'label_ids',
           'start_positions',
           'start_position',
           'end_positions',
           'end_position',
           'next_sentence_label',
       }
       __UpperCAmelCase	:  Dict		=     possible_label_cols.intersection(set(_UpperCAmelCase							)							)
       self.assertGreater(len(_UpperCAmelCase							)  ,  0  ,  msg='No matching label names found!'							)
       __UpperCAmelCase	:  List[Any]		=     {key: val for key, val in prepared_for_class.items() if key in label_names}
       __UpperCAmelCase	:  Dict		=     {key: val for key, val in prepared_for_class.items() if key not in label_names}
       self.assertGreater(len(_UpperCAmelCase							)  ,  0							)

       # Make sure it works with XLA!
       model.compile(optimizer=tf.keras.optimizers.SGD(0.0							)  ,  jit_compile=_UpperCAmelCase							)
       # Make sure the model fits without crashing regardless of where we pass the labels
       __UpperCAmelCase	:  Optional[int]		=     model.fit(
           _UpperCAmelCase  ,  validation_data=_UpperCAmelCase  ,  steps_per_epoch=1  ,  validation_steps=1  ,  shuffle=_UpperCAmelCase  ,  verbose=0  ,  )
       __UpperCAmelCase	:  Dict		=     history.history['loss'][0]
       self.assertTrue(not isnan(_UpperCAmelCase							)							)
       __UpperCAmelCase	:  List[str]		=     history.history['val_loss'][0]
       self.assertTrue(not isnan(_UpperCAmelCase							)							)

       # Now test it with separate labels, to make sure that path works in XLA too.
       __UpperCAmelCase	:  Union[str, Any]		=     model_class(_UpperCAmelCase							)
       model.compile(optimizer=tf.keras.optimizers.SGD(0.0							)  ,  jit_compile=_UpperCAmelCase							)
       __UpperCAmelCase	:  str		=     model.fit(
           _UpperCAmelCase  ,  _UpperCAmelCase  ,  validation_data=(inputs_minus_labels, labels)  ,  steps_per_epoch=1  ,  validation_steps=1  ,  shuffle=_UpperCAmelCase  ,  verbose=0  ,  )

       __UpperCAmelCase	:  List[str]		=     history.history['loss'][0]
       self.assertTrue(not isnan(_UpperCAmelCase							)							)
       __UpperCAmelCase	:  Union[str, Any]		=     history.history['val_loss'][0]
       self.assertTrue(not isnan(_UpperCAmelCase							)							)




 @slow
 def     a       (			self							):
   __UpperCAmelCase,			__UpperCAmelCase	:  List[Any]		=     self.model_tester.prepare_config_and_inputs_for_common()
   __UpperCAmelCase	:  Dict		=     True
   __UpperCAmelCase	:  List[Any]		=     True

   if hasattr(_UpperCAmelCase  ,  'use_cache'							):
     __UpperCAmelCase	:  int		=     True

   __UpperCAmelCase	:  List[str]		=     getattr(self.model_tester  ,  'encoder_seq_length'  ,  self.model_tester.seq_length							)
   __UpperCAmelCase	:  int		=     getattr(self.model_tester  ,  'key_length'  ,  _UpperCAmelCase							)

   for model_class in self.all_model_classes[:2]:
     __UpperCAmelCase	:  Tuple		=     self._prepare_for_class(_UpperCAmelCase  ,  _UpperCAmelCase							)
     __UpperCAmelCase	:  Union[str, Any]		=     model_class(_UpperCAmelCase							)
     model.build()
     __UpperCAmelCase	:  Union[str, Any]		=     len(model(_UpperCAmelCase							)							)

     for key in list(class_inputs_dict.keys()							):
       # Remove keys not in the serving signature, as the SavedModel will not be compiled to deal with them
       if key not in model.input_signature:
         del class_inputs_dict[key]
       # Check it's a tensor, in case the inputs dict has some bools in it too
       elif isinstance(class_inputs_dict[key]  ,  tf.Tensor							) and class_inputs_dict[key].dtype.is_integer:
         __UpperCAmelCase	:  Any		=     tf.cast(class_inputs_dict[key]  ,  tf.intaa							)

     if set(class_inputs_dict.keys()							) != set(model.input_signature.keys()							):
       continue  # Some models have inputs that the preparation functions don't create, we skip those

     with tempfile.TemporaryDirectory() as tmpdirname:
       model.save_pretrained(_UpperCAmelCase  ,  saved_model=_UpperCAmelCase							)
       __UpperCAmelCase	:  List[Any]		=     os.path.join(_UpperCAmelCase  ,  'saved_model'  ,  '1'							)
       __UpperCAmelCase	:  int		=     tf.keras.models.load_model(_UpperCAmelCase							)
       __UpperCAmelCase	:  List[str]		=     model(_UpperCAmelCase							)

       if self.is_encoder_decoder:
         __UpperCAmelCase	:  List[str]		=     outputs['encoder_hidden_states']
         __UpperCAmelCase	:  List[Any]		=     outputs['encoder_attentions']
       else:
         __UpperCAmelCase	:  Dict		=     outputs['hidden_states']
         __UpperCAmelCase	:  Optional[Any]		=     outputs['attentions']

       self.assertEqual(len(_UpperCAmelCase							)  ,  _UpperCAmelCase							)

       __UpperCAmelCase	:  Any		=     getattr(
           self.model_tester  ,  'expected_num_hidden_layers'  ,  self.model_tester.num_hidden_layers + 1							)

       self.assertEqual(len(_UpperCAmelCase							)  ,  _UpperCAmelCase							)
       self.assertListEqual(
           list(output_hidden_states[0].shape[-2:]							)  ,  [self.model_tester.seq_length, self.model_tester.hidden_size]  ,  )

       self.assertEqual(len(_UpperCAmelCase							)  ,  self.model_tester.num_hidden_layers							)
       self.assertListEqual(
           list(output_attentions[0].shape[-3:]							)  ,  [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length]  ,  )




 @slow
 def     a       (			self							):
   tf.keras.mixed_precision.set_global_policy('mixed_float16'							)

   # try/finally block to ensure subsequent tests run in float32
   try:
     __UpperCAmelCase,			__UpperCAmelCase	:  List[str]		=     self.model_tester.prepare_config_and_inputs_for_common()
     for model_class in self.all_model_classes[:2]:
       __UpperCAmelCase	:  List[str]		=     self._prepare_for_class(_UpperCAmelCase  ,  _UpperCAmelCase							)
       __UpperCAmelCase	:  Optional[int]		=     model_class(_UpperCAmelCase							)
       __UpperCAmelCase	:  int		=     model(_UpperCAmelCase							)

       self.assertIsNotNone(_UpperCAmelCase							)
   finally:
     tf.keras.mixed_precision.set_global_policy('float32'							)




 @slow
 def     a       (			self							):
   __UpperCAmelCase,			__UpperCAmelCase	:  Optional[Any]		=     self.model_tester.prepare_config_and_inputs_for_common()
   # head_mask and decoder_head_mask has different shapes than other input args
   if "head_mask" in inputs_dict:
     del inputs_dict["head_mask"]
   if "decoder_head_mask" in inputs_dict:
     del inputs_dict["decoder_head_mask"]
   if "cross_attn_head_mask" in inputs_dict:
     del inputs_dict["cross_attn_head_mask"]
   __UpperCAmelCase	:  Union[str, Any]		=     {
       module_member
       for model_class in self.all_model_classes
       for module in (import_module(model_class.__module__							),)
       for module_member_name in dir(_UpperCAmelCase							)
       if module_member_name.endswith('MainLayer'							)
       for module_member in (getattr(_UpperCAmelCase  ,  _UpperCAmelCase							),)
       if isinstance(_UpperCAmelCase  ,  _UpperCAmelCase							)
       and tf.keras.layers.Layer in module_member.__bases__
       and getattr(_UpperCAmelCase  ,  '_keras_serializable'  ,  _UpperCAmelCase							)
   }

   for main_layer_class in tf_main_layer_classes:
     # T5MainLayer needs an embed_tokens parameter when called without the inputs_embeds parameter
     if "T5" in main_layer_class.__name__:
       # Take the same values than in TFT5ModelTester for this shared layer
       __UpperCAmelCase	:  List[str]		=     TFSharedEmbeddings(self.model_tester.vocab_size  ,  self.model_tester.hidden_size  ,  name='shared'							)
       __UpperCAmelCase	:  Optional[Any]		=     False
       __UpperCAmelCase	:  Optional[int]		=     main_layer_class(_UpperCAmelCase  ,  embed_tokens=_UpperCAmelCase							)
     else:
       __UpperCAmelCase	:  Union[str, Any]		=     main_layer_class(_UpperCAmelCase							)

     __UpperCAmelCase	:  Union[str, Any]		=     {
         name: tf.keras.Input(tensor.shape[1:]  ,  dtype=tensor.dtype							) for name, tensor in inputs_dict.items()
     }

     if hasattr(self.model_tester  ,  'num_labels'							):
       __UpperCAmelCase	:  str		=     self.model_tester.num_labels
     else:
       __UpperCAmelCase	:  Tuple		=     2

     __UpperCAmelCase	:  Tuple		=     tf.data.Dataset.from_tensor_slices(
         (inputs_dict, np.ones((self.model_tester.batch_size, self.model_tester.seq_length, num_labels, 1)							))							).batch(1							)

     __UpperCAmelCase	:  Optional[Any]		=     main_layer(_UpperCAmelCase							)[0]
     __UpperCAmelCase	:  Dict		=     tf.keras.layers.Dense(_UpperCAmelCase  ,  activation='softmax'  ,  name='outputs'							)(_UpperCAmelCase							)
     __UpperCAmelCase	:  Dict		=     tf.keras.models.Model(inputs=_UpperCAmelCase  ,  outputs=[outputs]							)

     model.compile(loss='binary_crossentropy'  ,  optimizer='adam'  ,  metrics=['binary_accuracy']							)
     model.fit(_UpperCAmelCase  ,  epochs=1							)

     with tempfile.TemporaryDirectory() as tmpdirname:
       __UpperCAmelCase	:  Optional[Any]		=     os.path.join(_UpperCAmelCase  ,  'keras_model.h5'							)
       model.save(_UpperCAmelCase							)
       if "T5" in main_layer_class.__name__:
         __UpperCAmelCase	:  int		=     tf.keras.models.load_model(
             _UpperCAmelCase  ,  custom_objects={
                 main_layer_class.__name__: main_layer_class,
                 'TFSharedEmbeddings': TFSharedEmbeddings,
             }  ,  )
       else:
         __UpperCAmelCase	:  List[Any]		=     tf.keras.models.load_model(
             _UpperCAmelCase  ,  custom_objects={main_layer_class.__name__: main_layer_class}							)
       assert isinstance(_UpperCAmelCase  ,  tf.keras.Model							)
       model(_UpperCAmelCase							)




 @slow
 def     a       (			self							):
   __UpperCAmelCase,			__UpperCAmelCase	:  str		=     self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes[:2]:
     __UpperCAmelCase	:  Optional[int]		=     model_class(_UpperCAmelCase							)

     __UpperCAmelCase	:  Dict		=     copy.deepcopy(_UpperCAmelCase							)

     if not self.is_encoder_decoder:
       __UpperCAmelCase	:  Union[str, Any]		=     inputs['input_ids']
       del inputs["input_ids"]
     else:
       __UpperCAmelCase	:  Optional[int]		=     inputs['input_ids']
       __UpperCAmelCase	:  str		=     inputs.get('decoder_input_ids'  ,  _UpperCAmelCase							)
       del inputs["input_ids"]
       inputs.pop('decoder_input_ids'  ,  _UpperCAmelCase							)

     if not self.is_encoder_decoder:
       __UpperCAmelCase	:  List[str]		=     model.get_input_embeddings()(_UpperCAmelCase							)
     else:
       __UpperCAmelCase	:  Union[str, Any]		=     model.get_input_embeddings()(_UpperCAmelCase							)
       __UpperCAmelCase	:  Tuple		=     model.get_input_embeddings()(_UpperCAmelCase							)

     __UpperCAmelCase	:  Any		=     self._prepare_for_class(_UpperCAmelCase  ,  _UpperCAmelCase							)

     @tf.function
     def run_in_graph_mode():
       return model(_UpperCAmelCase							)

     __UpperCAmelCase	:  int		=     run_in_graph_mode()
     self.assertIsNotNone(_UpperCAmelCase							)




 def     a       (			self  ,  _UpperCAmelCase  ,  _UpperCAmelCase							):
   # special tokens cannot be bad tokens
   __UpperCAmelCase	:  Any		=     []
   if model.config.bos_token_id is not None:
     special_tokens.append(model.config.bos_token_id							)
   if model.config.pad_token_id is not None:
     special_tokens.append(model.config.pad_token_id							)
   if model.config.eos_token_id is not None:
     special_tokens.append(model.config.eos_token_id							)

   # create random bad tokens that are not special tokens
   __UpperCAmelCase	:  int		=     []
   while len(_UpperCAmelCase							) < num_bad_tokens:
     __UpperCAmelCase	:  Optional[Any]		=     tf.squeeze(ids_tensor((1, 1)  ,  self.model_tester.vocab_size							)  ,  0							).numpy()[0]
     if token not in special_tokens:
       bad_tokens.append(_UpperCAmelCase							)
   return bad_tokens




 def     a       (			self  ,  _UpperCAmelCase							):
   for token_id in output_ids[0].numpy().tolist():
     self.assertGreaterEqual(_UpperCAmelCase  ,  0							)
     self.assertLess(_UpperCAmelCase  ,  self.model_tester.vocab_size							)




 def     a       (			self  ,  _UpperCAmelCase  ,  _UpperCAmelCase							):
   # for all bad word tokens
   for bad_word_ids in bad_words_ids:
     # for all slices in batch
     for generated_ids_slice in generated_ids:
       # for all word idx
       for i in range(len(_UpperCAmelCase							)  ,  len(_UpperCAmelCase							)							):
         # if tokens match
         if generated_ids_slice[i - len(_UpperCAmelCase							) : i] == bad_word_ids:
           return True
   return False








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(4_2)

_lowerCamelCase							:  Tuple     =				get_tests_dir("fixtures")
_lowerCamelCase							:  List[Any]     =				os.path.join(dirname(get_tests_dir()))
_lowerCamelCase							:  List[Any]     =				dirname(os.path.abspath(__file__))

# default torch.distributed port
_lowerCamelCase							:  Any     =				"10999"

_lowerCamelCase							:  Dict     =				"t5-small"

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


# *** To Fix ***


# *** tiny model issues ***
# missing model files:
_lowerCamelCase							:  List[str]     =				"hf-internal-testing/tiny-random-mt5"
_lowerCamelCase							:  List[Any]     =				"hf-internal-testing/tiny-random-camembert"
_lowerCamelCase							:  str     =				"hf-internal-testing/tiny-random-openai-gpt"

# missing tokenizer files
_lowerCamelCase							:  Tuple     =				"hf-internal-testing/tiny-random-convbert"
_lowerCamelCase							:  Any     =				"hf-internal-testing/tiny-random-layoutlmv2"
_lowerCamelCase							:  str     =				"hf-internal-testing/tiny-random-hubert"

# issues with tokenizer
_lowerCamelCase							:  List[str]     =				"hf-internal-testing/tiny-random-ctrl"
_lowerCamelCase							:  Any     =				"hf-internal-testing/tiny-random-transfo-xl"  # same as ctrl

# other issues with tiny models
_lowerCamelCase							:  List[str]     =				"hf-internal-testing/tiny-random-ibert"  # multiple issues with either mlm/qa/clas
_lowerCamelCase							:  List[Any]     =				"hf-internal-testing/tiny-random-reformer"  # multiple issues with either mlm/qa/clas

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

def      a__	(						UpperCAmelCase				:     Any=False     )    ->		Optional[Any]:
  # 1. explicitly set --num_nodes=1 just in case these tests end up run on a multi-node setup
  # - it won't be able to handle that
  # 2. for now testing with just 2 gpus max (since some quality tests may give different
  # results with mode gpus because we use very little data)
  UpperCAmelCase			:				Union[str, Any]										=					min(2							,					get_gpu_count()     ) if distributed else 1
  UpperCAmelCase			:				List[Any]										=					os.environ.get('''DS_TEST_PORT'''							,					UpperCAmelCase     )
  return f'''deepspeed --num_nodes 1 --num_gpus {num_gpus} --master_port {master_port}'''.split()

def      a__	(						)    ->		List[Any]:
  UpperCAmelCase			:				List[str]										=					f'''{FIXTURE_DIRECTORY}/tests_samples'''
  UpperCAmelCase			:				Optional[Any]										=					f'''{data_dir_samples}/wmt_en_ro'''
  UpperCAmelCase			:				int										=					f'''{data_dir_samples}/xsum'''
  UpperCAmelCase			:				int										=					'''
        --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			:				str										=					{
      '''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			:				int										=					f'''{ROOT_DIRECTORY}/examples/pytorch'''

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

  UpperCAmelCase			:				Dict										=					get_launcher(distributed=UpperCAmelCase     )

  UpperCAmelCase			:				Tuple										=					{}
  for task, args in tasks.items():
    UpperCAmelCase			:				str										=					args.split()
    for model in tasksamodels[task]:
      UpperCAmelCase			:				List[str]										=					globals()[f'''{model.upper().replace("-"							,					"_"     )}_TINY''']
      UpperCAmelCase			:				Tuple										=					f'''--model_name_or_path {model_name}'''.split()
      UpperCAmelCase			:				List[str]										=					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


_lowerCamelCase							:  Optional[Any]     =				make_task_cmds()

_lowerCamelCase							:  Any     =				"zero2"
_lowerCamelCase							:  Optional[int]     =				"zero3"

_lowerCamelCase							:  Optional[int]     =				[ZEROa, ZEROa]

def      a__	(						UpperCAmelCase				:     Dict							,					UpperCAmelCase				:     Dict							,					UpperCAmelCase				:     Dict     )    ->		str:
  # customize the test name generator function as we want both params to appear in the sub-test
  # name, as by default it shows only the first param
  UpperCAmelCase			:				List[str]										=					parameterized.to_safe_name('''_'''.join(str(UpperCAmelCase     ) for x in param.args     )     )
  return f'''{func.__name__}_{param_based_name}'''


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






@slow
@require_deepspeed
@require_torch_gpu
class    __UpperCAmelCase	(			lowerCamelCase__		):



   def 			__magic_name__       (				self				:	Optional[int],						__A				:	Union[str, Any],						__A				:	str		):
     # return a ready to run train cmd
     if task not in task_cmds:
       raise ValueError(F'''don\'t know of task {task}, have {task_cmds.keys()}'''		)

     UpperCAmelCase			:				Optional[Any]										=					task_cmds[task]
     UpperCAmelCase			:				int										=					F'''--deepspeed {self.test_file_dir_str}/ds_config_{stage}.json'''.split()

     UpperCAmelCase			:				Optional[int]										=					self.get_auto_remove_tmp_dir()
     UpperCAmelCase			:				int										=					F'''--output_dir {output_dir}'''.split()

     cmd += args_ds + args_out

     return cmd, output_dir





   @parameterized.expand(__A,						name_func=__A		)
   def 			__magic_name__       (				self				:	Tuple,						__A				:	List[Any],						__A				:	List[str]		):
     # testing the ability to do a run followed by recovery of full fp32 weights

     UpperCAmelCase					,		UpperCAmelCase			:				List[str]										=					self.get_task_cmd(__A,						__A		)

     # 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(__A,						env=self.get_env()		)

     # 2. test that the fp32 weights get reconsolidated
     UpperCAmelCase			:				Tuple										=					F'''{output_dir}/checkpoint-1'''
     UpperCAmelCase			:				List[Any]										=					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(__A,						shell=__A		)
     assert os.path.exists(__A		), 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





