
import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import OPTConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
						import os

						# The slow tests are often failing with OOM error on GPU
						# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
						# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
						__a        = 'platform'

						import jax
						import jax.numpy as jnp

						from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer




def     a						(							snake_case__: int						,       snake_case__: List[Any]						,       snake_case__: Dict=None						,       snake_case__: int=None ):


				'''simple docstring'''
				if attention_mask is None:
								lowercase_					  = np.where(input_ids != config.pad_token_id						,       1						,       0 )
				return {
				    "input_ids": input_ids,
				    "attention_mask": attention_mask,
				}
@require_flax
class 			lowercase__:



	"""simple docstring"""




	def __init__(	self  :      Optional[Any]	,							SCREAMING_SNAKE_CASE_  :      Optional[int]	,							SCREAMING_SNAKE_CASE_  :      List[Any]=1_3	,							SCREAMING_SNAKE_CASE_  :      str=7	,							SCREAMING_SNAKE_CASE_  :      Tuple=True	,							SCREAMING_SNAKE_CASE_  :      Union[str, Any]=False	,							SCREAMING_SNAKE_CASE_  :      List[Any]=9_9	,							SCREAMING_SNAKE_CASE_  :      Optional[Any]=1_6	,							SCREAMING_SNAKE_CASE_  :      List[str]=2	,							SCREAMING_SNAKE_CASE_  :      int=4	,							SCREAMING_SNAKE_CASE_  :      Dict=4	,							SCREAMING_SNAKE_CASE_  :      str="gelu"	,							SCREAMING_SNAKE_CASE_  :      int=0.1	,							SCREAMING_SNAKE_CASE_  :      Any=0.1	,							SCREAMING_SNAKE_CASE_  :      Optional[int]=2_0	,							SCREAMING_SNAKE_CASE_  :      str=2	,							SCREAMING_SNAKE_CASE_  :      List[Any]=1	,							SCREAMING_SNAKE_CASE_  :      List[Any]=0	,							SCREAMING_SNAKE_CASE_  :      List[str]=1_6	,							SCREAMING_SNAKE_CASE_  :      Optional[int]=1_6	,							SCREAMING_SNAKE_CASE_  :      Optional[int]=0.02	,							)	->					Tuple:
					lowercase_					  = parent
					lowercase_					  = batch_size
					lowercase_					  = seq_length
					lowercase_					  = is_training
					lowercase_					  = use_labels
					lowercase_					  = vocab_size
					lowercase_					  = hidden_size
					lowercase_					  = num_hidden_layers
					lowercase_					  = num_attention_heads
					lowercase_					  = intermediate_size
					lowercase_					  = hidden_act
					lowercase_					  = hidden_dropout_prob
					lowercase_					  = attention_probs_dropout_prob
					lowercase_					  = max_position_embeddings
					lowercase_					  = eos_token_id
					lowercase_					  = pad_token_id
					lowercase_					  = bos_token_id
					lowercase_					  = embed_dim
					lowercase_					  = word_embed_proj_dim
					lowercase_					  = initializer_range
					lowercase_					  = False
	def 						_lowercase  (	self  :      str	)	->					List[Any]:
					lowercase_					  = np.clip(ids_tensor([self.batch_size, self.seq_length - 1]	,							self.vocab_size	)	,							3	,							self.vocab_size	)
					lowercase_					  = np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)	,							dtype=np.intaa	))	,							-1	)

					lowercase_					  = OPTConfig(
					    vocab_size=self.vocab_size	,							hidden_size=self.hidden_size	,							num_hidden_layers=self.num_hidden_layers	,							num_attention_heads=self.num_attention_heads	,							ffn_dim=self.intermediate_size	,							dropout=self.hidden_dropout_prob	,							attention_dropout=self.attention_probs_dropout_prob	,							max_position_embeddings=self.max_position_embeddings	,							eos_token_id=self.eos_token_id	,							bos_token_id=self.bos_token_id	,							pad_token_id=self.pad_token_id	,							embed_dim=self.embed_dim	,							is_encoder_decoder=SCREAMING_SNAKE_CASE_	,							word_embed_proj_dim=self.word_embed_proj_dim	,							initializer_range=self.initializer_range	,							use_cache=SCREAMING_SNAKE_CASE_	,							)
					lowercase_					  = prepare_opt_inputs_dict(SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	)
					return config, inputs_dict
	def 						_lowercase  (	self  :      Any	)	->					Union[str, Any]:
					lowercase_  ,     lowercase_					  = self.prepare_config_and_inputs()
					return config, inputs_dict
	def 						_lowercase  (	self  :      str	,							SCREAMING_SNAKE_CASE_  :      Optional[int]	,							SCREAMING_SNAKE_CASE_  :      Optional[int]	,							SCREAMING_SNAKE_CASE_  :      Any	)	->					Dict:
					lowercase_					  = 2_0
					lowercase_					  = model_class_name(SCREAMING_SNAKE_CASE_	)

					lowercase_					  = inputs_dict['''input_ids''']
					lowercase_					  = inputs_dict['''attention_mask''']

					lowercase_					  = model.init_cache(input_ids.shape[0]	,							SCREAMING_SNAKE_CASE_	)
					lowercase_					  = jnp.ones((input_ids.shape[0], max_length)	,							dtype='''i4'''	)

					lowercase_					  = jnp.broadcast_to(
					    jnp.arange(input_ids.shape[-1] - 1	)[None, :]	,							(input_ids.shape[0], input_ids.shape[-1] - 1)	,							)
					lowercase_					  = model(
					    input_ids[:, :-1]	,							attention_mask=SCREAMING_SNAKE_CASE_	,							past_key_values=SCREAMING_SNAKE_CASE_	,							position_ids=SCREAMING_SNAKE_CASE_	,							)

					lowercase_					  = jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]	,							dtype='''i4'''	)
					lowercase_					  = model(
					    input_ids[:, -1:]	,							attention_mask=SCREAMING_SNAKE_CASE_	,							past_key_values=outputs_cache.past_key_values	,							position_ids=SCREAMING_SNAKE_CASE_	,							)

					lowercase_					  = model(SCREAMING_SNAKE_CASE_	)

					lowercase_					  = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])	)	)
					self.parent.assertTrue(diff < 1e-3	,							msg=f'''Max diff is {diff}'''	)




	def 						_lowercase  (	self  :      Dict	,							SCREAMING_SNAKE_CASE_  :      Tuple	,							SCREAMING_SNAKE_CASE_  :      List[Any]	,							SCREAMING_SNAKE_CASE_  :      Dict	)	->					Dict:
					lowercase_					  = 2_0
					lowercase_					  = model_class_name(SCREAMING_SNAKE_CASE_	)

					lowercase_  ,     lowercase_					  = (
					    inputs_dict['''input_ids'''],
					    inputs_dict['''attention_mask'''],
					)

					lowercase_					  = jnp.concatenate(
					    [
					        attention_mask,
					        jnp.zeros((attention_mask.shape[0], max_length - attention_mask.shape[1])	),
					    ]	,							axis=-1	,							)

					lowercase_					  = model.init_cache(input_ids.shape[0]	,							SCREAMING_SNAKE_CASE_	)
					lowercase_					  = jnp.broadcast_to(
					    jnp.arange(input_ids.shape[-1] - 1	)[None, :]	,							(input_ids.shape[0], input_ids.shape[-1] - 1)	,							)

					lowercase_					  = model(
					    input_ids[:, :-1]	,							attention_mask=SCREAMING_SNAKE_CASE_	,							past_key_values=SCREAMING_SNAKE_CASE_	,							position_ids=SCREAMING_SNAKE_CASE_	,							)
					lowercase_					  = jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]	,							dtype='''i4'''	)
					lowercase_					  = model(
					    input_ids[:, -1:]	,							past_key_values=outputs_cache.past_key_values	,							attention_mask=SCREAMING_SNAKE_CASE_	,							position_ids=SCREAMING_SNAKE_CASE_	,							)

					lowercase_					  = model(SCREAMING_SNAKE_CASE_	,							attention_mask=SCREAMING_SNAKE_CASE_	)

					lowercase_					  = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])	)	)
					self.parent.assertTrue(diff < 1e-3	,							msg=f'''Max diff is {diff}'''	)

@require_flax
class 			lowercase__(    UpperCAmelCase			,						unittest.TestCase			,						UpperCAmelCase							):



	"""simple docstring"""




	a      :Optional[int]   					=     (FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
	a      :int   					=     () if is_flax_available() else ()
	def 						_lowercase  (	self  :      Tuple	)	->					Optional[int]:
					lowercase_					  = FlaxOPTModelTester(self	)
	def 						_lowercase  (	self  :      int	)	->					Dict:
					lowercase_  ,     lowercase_					  = self.model_tester.prepare_config_and_inputs()
					for model_class in self.all_model_classes:
									self.model_tester.check_use_cache_forward(SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	)
	def 						_lowercase  (	self  :      Tuple	)	->					Optional[int]:
					lowercase_  ,     lowercase_					  = self.model_tester.prepare_config_and_inputs()
					for model_class in self.all_model_classes:
									self.model_tester.check_use_cache_forward_with_attn_mask(SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	)




	@slow
	def 						_lowercase  (	self  :      str	)	->					Union[str, Any]:
					for model_class_name in self.all_model_classes:
									lowercase_					  = model_class_name.from_pretrained('''facebook/opt-125m'''	)
									lowercase_					  = np.ones((1, 1)	) * model.config.eos_token_id
									lowercase_					  = model(SCREAMING_SNAKE_CASE_	)
									self.assertIsNotNone(SCREAMING_SNAKE_CASE_	)

@require_sentencepiece
@require_flax
class 			lowercase__(    unittest.TestCase							):



	"""simple docstring"""




	@slow
	def 						_lowercase  (	self  :      str	)	->					Optional[int]:
					lowercase_					  = FlaxOPTModel.from_pretrained('''facebook/opt-350m'''	)
					lowercase_					  = jnp.array([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]	)
					lowercase_					  = model(input_ids=SCREAMING_SNAKE_CASE_	).last_hidden_state
					lowercase_					  = (1, 1_1, 5_1_2)
					self.assertEqual(output.shape	,							SCREAMING_SNAKE_CASE_	)
					lowercase_					  = jnp.array(
					    [[-0.28_67, -1.92_56, -0.30_62], [-1.27_11, -0.13_37, -0.18_97], [0.41_09, 0.11_87, -1.31_42]]	)
					self.assertTrue(jnp.allclose(output[:, :3, :3]	,							SCREAMING_SNAKE_CASE_	,							atol=4e-2	)	)

@require_flax
@slow
class 			lowercase__(    unittest.TestCase							):



	"""simple docstring"""




	def 						_lowercase  (	self  :      Optional[int]	)	->					Union[str, Any]:
					super().setUp()
					lowercase_					  = '''facebook/opt-350m'''




	def 						_lowercase  (	self  :      Union[str, Any]	)	->					Dict:
					lowercase_					  = FlaxOPTForCausalLM.from_pretrained(self.path_model	)
					lowercase_					  = GPTaTokenizer.from_pretrained(self.path_model	)

					lowercase_					  = [
					    '''Today is a beautiful day and I want to''',
					    '''In the city of''',
					    '''Paris is the capital of France and''',
					    '''Computers and mobile phones have taken''',
					]
					# verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
					lowercase_					  = tokenizer(SCREAMING_SNAKE_CASE_	,							return_tensors='''jax'''	,							padding=SCREAMING_SNAKE_CASE_	,							add_special_tokens=SCREAMING_SNAKE_CASE_	)
					lowercase_					  = model(inputs.input_ids	,							attention_mask=inputs.attention_mask	)[0].mean(axis=-1	)
					lowercase_					  = jnp.array(
					    [
					        [1.38_51, -13.89_23, -10.52_29, -10.75_33, -0.23_09, -10.23_84, -0.53_65, -9.09_47, -5.16_70],
					        [-4.70_73, -10.62_76, -3.94_15, -21.52_42, -0.28_22, -0.28_22, -0.28_22, -0.28_22, -0.28_22],
					        [0.62_47, -3.42_29, -8.91_79, -1.42_97, -14.16_50, 1.41_46, -9.02_18, -0.27_03, -0.27_03],
					        [6.47_83, -1.99_13, -10.79_26, -2.33_36, 1.50_92, -0.99_74, -6.82_13, 1.34_77, 1.34_77],
					    ]	)
					self.assertTrue(jnp.allclose(SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	,							atol=4e-2	)	)

					lowercase_					  = jax.jit(SCREAMING_SNAKE_CASE_	)
					lowercase_					  = model(inputs.input_ids	,							attention_mask=inputs.attention_mask	)[0].mean(axis=-1	)
					self.assertTrue(jnp.allclose(SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	,							atol=4e-2	)	)

@require_flax
@slow
class 			lowercase__(    unittest.TestCase							):



	"""simple docstring"""




	@property
	def 						_lowercase  (	self  :      List[Any]	)	->					Union[str, Any]:
					return [
					    "Today is a beautiful day and I want",
					    "In the city of",
					    "Paris is the capital of France and",
					    "Computers and mobile phones have taken",
					]
	def 						_lowercase  (	self  :      Optional[int]	)	->					str:
					lowercase_					  = '''facebook/opt-125m'''

					lowercase_					  = [
					    '''Today is a beautiful day and I want to''',
					    '''In the city of New York, the city''',
					    '''Paris is the capital of France and the capital''',
					    '''Computers and mobile phones have taken over the''',
					]

					lowercase_					  = []

					lowercase_					  = FlaxOPTForCausalLM.from_pretrained(SCREAMING_SNAKE_CASE_	)
					lowercase_					  = GPTaTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_	)

					for prompt in self.prompts:
									lowercase_					  = tokenizer(SCREAMING_SNAKE_CASE_	,							return_tensors='''jax'''	).input_ids

									lowercase_					  = model.generate(SCREAMING_SNAKE_CASE_	,							max_length=1_0	)
									lowercase_					  = generated_ids[0]

									lowercase_					  = tokenizer.batch_decode(SCREAMING_SNAKE_CASE_	,							skip_special_tokens=SCREAMING_SNAKE_CASE_	)
									predicted_outputs += generated_string

					self.assertListEqual(SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	)
	def 						_lowercase  (	self  :      Union[str, Any]	)	->					Union[str, Any]:
					lowercase_					  = '''facebook/opt-350m'''

					lowercase_					  = [
					    '''Today is a beautiful day and I want to''',
					    '''In the city of San Francisco, the city''',
					    '''Paris is the capital of France and the capital''',
					    '''Computers and mobile phones have taken over the''',
					]

					lowercase_					  = []
					lowercase_					  = FlaxOPTForCausalLM.from_pretrained(SCREAMING_SNAKE_CASE_	)
					lowercase_					  = GPTaTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_	)

					for prompt in self.prompts:
									lowercase_					  = tokenizer(SCREAMING_SNAKE_CASE_	,							return_tensors='''jax'''	).input_ids

									lowercase_					  = model.generate(SCREAMING_SNAKE_CASE_	,							max_length=1_0	)
									lowercase_					  = generated_ids[0]

									lowercase_					  = tokenizer.batch_decode(SCREAMING_SNAKE_CASE_	,							skip_special_tokens=SCREAMING_SNAKE_CASE_	)
									predicted_outputs += generated_string

					self.assertListEqual(SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	)
	def 						_lowercase  (	self  :      List[Any]	)	->					Union[str, Any]:
					lowercase_					  = '''facebook/opt-125m'''
					lowercase_					  = [
					    '''Today is a beautiful day and I want to thank''',
					    '''In the city of Rome Canaver Canaver Canaver Canaver''',
					]
					lowercase_					  = FlaxOPTForCausalLM.from_pretrained(SCREAMING_SNAKE_CASE_	)
					lowercase_					  = GPTaTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_	)
					lowercase_					  = tokenizer(
					    [
					        '''Today is a beautiful day and I want to''',
					        '''In the city of''',
					    ]	,							return_tensors='''jax'''	,							padding=SCREAMING_SNAKE_CASE_	,							)

					lowercase_					  = jax.jit(model.generate	)

					lowercase_					  = jit_generate(inputs['''input_ids''']	,							attention_mask=inputs['''attention_mask''']	).sequences

					lowercase_					  = tokenizer.batch_decode(SCREAMING_SNAKE_CASE_	,							skip_special_tokens=SCREAMING_SNAKE_CASE_	)

					self.assertIsNotNone(SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	)




	def 						_lowercase  (	self  :      Optional[int]	)	->					Dict:
					lowercase_					  = '''facebook/opt-350m'''

					lowercase_					  = GPTaTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_	)
					lowercase_					  = FlaxOPTForCausalLM.from_pretrained(SCREAMING_SNAKE_CASE_	)

					lowercase_					  = '''left'''

					# use different length sentences to test batching
					lowercase_					  = [
					    '''Hello, my dog is a little''',
					    '''Today, I''',
					]

					lowercase_					  = tokenizer(SCREAMING_SNAKE_CASE_	,							return_tensors='''jax'''	,							padding=SCREAMING_SNAKE_CASE_	)
					lowercase_					  = inputs['''input_ids''']

					lowercase_					  = model.generate(input_ids=SCREAMING_SNAKE_CASE_	,							attention_mask=inputs['''attention_mask''']	,							trace=SCREAMING_SNAKE_CASE_	)

					lowercase_					  = tokenizer(sentences[0]	,							return_tensors='''jax'''	).input_ids
					lowercase_					  = model.generate(input_ids=SCREAMING_SNAKE_CASE_	)

					lowercase_					  = inputs_non_padded.shape[-1] - inputs['''attention_mask'''][-1].sum()
					lowercase_					  = tokenizer(sentences[1]	,							return_tensors='''jax'''	).input_ids
					lowercase_					  = model.generate(input_ids=SCREAMING_SNAKE_CASE_	,							max_length=model.config.max_length - num_paddings	)

					lowercase_					  = tokenizer.batch_decode(outputs[0]	,							skip_special_tokens=SCREAMING_SNAKE_CASE_	)
					lowercase_					  = tokenizer.decode(output_non_padded[0][0]	,							skip_special_tokens=SCREAMING_SNAKE_CASE_	)
					lowercase_					  = tokenizer.decode(output_padded[0][0]	,							skip_special_tokens=SCREAMING_SNAKE_CASE_	)

					lowercase_					  = [
					    '''Hello, my dog is a little bit of a dork.\nI\'m a little bit''',
					    '''Today, I was in the middle of a conversation with a friend about the''',
					]
					self.assertListEqual(SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	)
					self.assertListEqual(SCREAMING_SNAKE_CASE_	,							[non_padded_sentence, padded_sentence]	)






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

import tensorflow as tf

from transformers.testing_utils import TestCasePlus, get_gpu_count, slow


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


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


logging.basicConfig(level=logging.DEBUG)

__a        = logging.getLogger()




def     a						(							):


				'''simple docstring'''
				lowercase_					  = argparse.ArgumentParser()
				parser.add_argument('''-f''' )
				lowercase_					  = parser.parse_args()
				return args.f




def     a						(							snake_case__: List[Any] ):


				'''simple docstring'''
				lowercase_					  = {}
				lowercase_					  = os.path.join(snake_case__						,       '''all_results.json''' )
				if os.path.exists(snake_case__ ):
								with open(snake_case__						,       '''r''' ) as f:
												lowercase_					  = json.load(snake_case__ )
				else:
								raise ValueError(F'''can\'t find {path}''' )
				return results




def     a						(							):


				'''simple docstring'''
				return bool(tf.config.list_physical_devices('''GPU''' ) )


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


class 			lowercase__(    UpperCAmelCase							):



	"""simple docstring"""




	@skip('''Skipping until shape inference for to_tf_dataset PR is merged.'''	)
	def 						_lowercase  (	self  :      Tuple	)	->					List[Any]:
					lowercase_					  = self.get_auto_remove_tmp_dir()
					lowercase_					  = f'''
            run_text_classification.py
            --model_name_or_path distilbert-base-uncased
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv
            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
            --do_train
            --do_eval
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --learning_rate=1e-4
            --max_steps=10
            --warmup_steps=2
            --seed=42
            --max_seq_length=128
            '''.split()

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

					with patch.object(SCREAMING_SNAKE_CASE_	,							'''argv'''	,							SCREAMING_SNAKE_CASE_	):
									run_text_classification.main()
									# Reset the mixed precision policy so we don't break other tests
									tf.keras.mixed_precision.set_global_policy('''float32'''	)
									lowercase_					  = get_results(SCREAMING_SNAKE_CASE_	)
									self.assertGreaterEqual(result['''eval_accuracy''']	,							0.75	)
	def 						_lowercase  (	self  :      Optional[Any]	)	->					int:
					lowercase_					  = self.get_auto_remove_tmp_dir()
					lowercase_					  = f'''
            run_clm.py
            --model_name_or_path distilgpt2
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --do_train
            --do_eval
            --block_size 128
            --per_device_train_batch_size 2
            --per_device_eval_batch_size 1
            --num_train_epochs 2
            --output_dir {tmp_dir}
            --overwrite_output_dir
            '''.split()

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

					with patch.object(SCREAMING_SNAKE_CASE_	,							'''argv'''	,							SCREAMING_SNAKE_CASE_	):
									run_clm.main()
									lowercase_					  = get_results(SCREAMING_SNAKE_CASE_	)
									self.assertLess(result['''eval_perplexity''']	,							1_0_0	)
	def 						_lowercase  (	self  :      Any	)	->					Union[str, Any]:
					lowercase_					  = self.get_auto_remove_tmp_dir()
					lowercase_					  = f'''
            run_mlm.py
            --model_name_or_path distilroberta-base
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --max_seq_length 64
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --do_train
            --do_eval
            --prediction_loss_only
            --num_train_epochs=1
            --learning_rate=1e-4
        '''.split()

					with patch.object(SCREAMING_SNAKE_CASE_	,							'''argv'''	,							SCREAMING_SNAKE_CASE_	):
									run_mlm.main()
									lowercase_					  = get_results(SCREAMING_SNAKE_CASE_	)
									self.assertLess(result['''eval_perplexity''']	,							4_2	)
	def 						_lowercase  (	self  :      Any	)	->					str:
					# 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'''
            run_ner.py
            --model_name_or_path bert-base-uncased
            --train_file tests/fixtures/tests_samples/conll/sample.json
            --validation_file tests/fixtures/tests_samples/conll/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --do_train
            --do_eval
            --warmup_steps=2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=2
            --num_train_epochs={epochs}
            --seed 7
        '''.split()

					with patch.object(SCREAMING_SNAKE_CASE_	,							'''argv'''	,							SCREAMING_SNAKE_CASE_	):
									run_ner.main()
									lowercase_					  = get_results(SCREAMING_SNAKE_CASE_	)
									self.assertGreaterEqual(result['''accuracy''']	,							0.75	)
	def 						_lowercase  (	self  :      Any	)	->					Optional[Any]:
					lowercase_					  = self.get_auto_remove_tmp_dir()
					lowercase_					  = f'''
            run_qa.py
            --model_name_or_path bert-base-uncased
            --version_2_with_negative
            --train_file tests/fixtures/tests_samples/SQUAD/sample.json
            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --max_steps=10
            --warmup_steps=2
            --do_train
            --do_eval
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
        '''.split()

					with patch.object(SCREAMING_SNAKE_CASE_	,							'''argv'''	,							SCREAMING_SNAKE_CASE_	):
									run_squad.main()
									lowercase_					  = get_results(SCREAMING_SNAKE_CASE_	)
									self.assertGreaterEqual(result['''f1''']	,							3_0	)
									self.assertGreaterEqual(result['''exact''']	,							3_0	)
	def 						_lowercase  (	self  :      str	)	->					Dict:
					lowercase_					  = self.get_auto_remove_tmp_dir()
					lowercase_					  = f'''
            run_swag.py
            --model_name_or_path bert-base-uncased
            --train_file tests/fixtures/tests_samples/swag/sample.json
            --validation_file tests/fixtures/tests_samples/swag/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --max_steps=20
            --warmup_steps=2
            --do_train
            --do_eval
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
        '''.split()

					with patch.object(SCREAMING_SNAKE_CASE_	,							'''argv'''	,							SCREAMING_SNAKE_CASE_	):
									run_swag.main()
									lowercase_					  = get_results(SCREAMING_SNAKE_CASE_	)
									self.assertGreaterEqual(result['''val_accuracy''']	,							0.8	)
	@slow
	def 						_lowercase  (	self  :      int	)	->					int:
					lowercase_					  = self.get_auto_remove_tmp_dir()
					lowercase_					  = f'''
            run_summarization.py
            --model_name_or_path t5-small
            --train_file tests/fixtures/tests_samples/xsum/sample.json
            --validation_file tests/fixtures/tests_samples/xsum/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --max_steps=50
            --warmup_steps=8
            --do_train
            --do_eval
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
        '''.split()

					with patch.object(SCREAMING_SNAKE_CASE_	,							'''argv'''	,							SCREAMING_SNAKE_CASE_	):
									run_summarization.main()
									lowercase_					  = get_results(SCREAMING_SNAKE_CASE_	)
									self.assertGreaterEqual(result['''rouge1''']	,							1_0	)
									self.assertGreaterEqual(result['''rouge2''']	,							2	)
									self.assertGreaterEqual(result['''rougeL''']	,							7	)
									self.assertGreaterEqual(result['''rougeLsum''']	,							7	)
	@slow
	def 						_lowercase  (	self  :      Dict	)	->					Any:
					lowercase_					  = self.get_auto_remove_tmp_dir()
					lowercase_					  = f'''
            run_translation.py
            --model_name_or_path Rocketknight1/student_marian_en_ro_6_1
            --source_lang en
            --target_lang ro
            --train_file tests/fixtures/tests_samples/wmt16/sample.json
            --validation_file tests/fixtures/tests_samples/wmt16/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --warmup_steps=8
            --do_train
            --do_eval
            --learning_rate=3e-3
            --num_train_epochs 12
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --source_lang en_XX
            --target_lang ro_RO
        '''.split()

					with patch.object(SCREAMING_SNAKE_CASE_	,							'''argv'''	,							SCREAMING_SNAKE_CASE_	):
									run_translation.main()
									lowercase_					  = get_results(SCREAMING_SNAKE_CASE_	)
									self.assertGreaterEqual(result['''bleu''']	,							3_0	)




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

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




