
import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import MBartConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

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 AutoTokenizer
						from transformers.models.mbart.modeling_flax_mbart import (
						    FlaxMBartForConditionalGeneration,
						    FlaxMBartForQuestionAnswering,
						    FlaxMBartForSequenceClassification,
						    FlaxMBartModel,
						    shift_tokens_right,
						)




def     a						(							snake_case__: str						,       snake_case__: int						,       snake_case__: str=None						,       snake_case__: Tuple=None						,       snake_case__: str=None						,       snake_case__: Optional[Any]=None						,       snake_case__: Union[str, Any]=None						,       snake_case__: str=None						,       ):


				'''simple docstring'''
				if attention_mask is None:
								lowercase_					  = np.where(input_ids != config.pad_token_id						,       1						,       0 )
				if decoder_attention_mask is None:
								lowercase_					  = np.where(decoder_input_ids != config.pad_token_id						,       1						,       0 )
				if head_mask is None:
								lowercase_					  = np.ones((config.encoder_layers, config.encoder_attention_heads) )
				if decoder_head_mask is None:
								lowercase_					  = np.ones((config.decoder_layers, config.decoder_attention_heads) )
				if cross_attn_head_mask is None:
								lowercase_					  = np.ones((config.decoder_layers, config.decoder_attention_heads) )
				return {
				    "input_ids": input_ids,
				    "decoder_input_ids": decoder_input_ids,
				    "attention_mask": attention_mask,
				    "decoder_attention_mask": decoder_attention_mask,
				}
class 			lowercase__(    unittest.TestCase							):



	"""simple docstring"""




	def __init__(	self  :      Union[str, Any]	,							SCREAMING_SNAKE_CASE_  :      List[Any]	,							SCREAMING_SNAKE_CASE_  :      List[str]=1_3	,							SCREAMING_SNAKE_CASE_  :      Dict=7	,							SCREAMING_SNAKE_CASE_  :      Union[str, Any]=True	,							SCREAMING_SNAKE_CASE_  :      Optional[Any]=False	,							SCREAMING_SNAKE_CASE_  :      str=9_9	,							SCREAMING_SNAKE_CASE_  :      Dict=1_6	,							SCREAMING_SNAKE_CASE_  :      Any=2	,							SCREAMING_SNAKE_CASE_  :      List[Any]=4	,							SCREAMING_SNAKE_CASE_  :      Tuple=4	,							SCREAMING_SNAKE_CASE_  :      int="gelu"	,							SCREAMING_SNAKE_CASE_  :      Optional[Any]=0.1	,							SCREAMING_SNAKE_CASE_  :      str=0.1	,							SCREAMING_SNAKE_CASE_  :      Any=3_2	,							SCREAMING_SNAKE_CASE_  :      int=2	,							SCREAMING_SNAKE_CASE_  :      Optional[int]=1	,							SCREAMING_SNAKE_CASE_  :      Union[str, Any]=0	,							SCREAMING_SNAKE_CASE_  :      Tuple=2	,							SCREAMING_SNAKE_CASE_  :      Optional[int]=0.02	,							)	->					Any:
					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_					  = decoder_start_token_id
					lowercase_					  = initializer_range
	def 						_lowercase  (	self  :      List[str]	)	->					Tuple:
					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_					  = shift_tokens_right(SCREAMING_SNAKE_CASE_	,							1	)

					lowercase_					  = MBartConfig(
					    vocab_size=self.vocab_size	,							d_model=self.hidden_size	,							encoder_layers=self.num_hidden_layers	,							decoder_layers=self.num_hidden_layers	,							encoder_attention_heads=self.num_attention_heads	,							decoder_attention_heads=self.num_attention_heads	,							encoder_ffn_dim=self.intermediate_size	,							decoder_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	,							decoder_start_token_id=self.decoder_start_token_id	,							initializer_range=self.initializer_range	,							use_cache=SCREAMING_SNAKE_CASE_	,							)
					lowercase_					  = prepare_mbart_inputs_dict(SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	)
					return config, inputs_dict
	def 						_lowercase  (	self  :      Any	)	->					Any:
					lowercase_  ,     lowercase_					  = self.prepare_config_and_inputs()
					return config, inputs_dict
	def 						_lowercase  (	self  :      List[Any]	,							SCREAMING_SNAKE_CASE_  :      List[str]	,							SCREAMING_SNAKE_CASE_  :      int	,							SCREAMING_SNAKE_CASE_  :      Optional[int]	)	->					Tuple:
					lowercase_					  = 2_0
					lowercase_					  = model_class_name(SCREAMING_SNAKE_CASE_	)

					lowercase_					  = model.encode(inputs_dict['''input_ids''']	)

					lowercase_  ,     lowercase_					  = (
					    inputs_dict['''decoder_input_ids'''],
					    inputs_dict['''decoder_attention_mask'''],
					)

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

					lowercase_					  = jnp.broadcast_to(
					    jnp.arange(decoder_input_ids.shape[-1] - 1	)[None, :]	,							(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)	,							)
					lowercase_					  = model.decode(
					    decoder_input_ids[:, :-1]	,							SCREAMING_SNAKE_CASE_	,							decoder_attention_mask=SCREAMING_SNAKE_CASE_	,							past_key_values=SCREAMING_SNAKE_CASE_	,							decoder_position_ids=SCREAMING_SNAKE_CASE_	,							)

					lowercase_					  = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]	,							dtype='''i4'''	)
					lowercase_					  = model.decode(
					    decoder_input_ids[:, -1:]	,							SCREAMING_SNAKE_CASE_	,							decoder_attention_mask=SCREAMING_SNAKE_CASE_	,							past_key_values=outputs_cache.past_key_values	,							decoder_position_ids=SCREAMING_SNAKE_CASE_	,							)

					lowercase_					  = model.decode(SCREAMING_SNAKE_CASE_	,							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_  :      int	,							SCREAMING_SNAKE_CASE_  :      Dict	)	->					Union[str, Any]:
					lowercase_					  = 2_0
					lowercase_					  = model_class_name(SCREAMING_SNAKE_CASE_	)

					lowercase_					  = model.encode(inputs_dict['''input_ids''']	)

					lowercase_  ,     lowercase_					  = (
					    inputs_dict['''decoder_input_ids'''],
					    inputs_dict['''decoder_attention_mask'''],
					)

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

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

					lowercase_					  = model.decode(
					    decoder_input_ids[:, :-1]	,							SCREAMING_SNAKE_CASE_	,							decoder_attention_mask=SCREAMING_SNAKE_CASE_	,							past_key_values=SCREAMING_SNAKE_CASE_	,							decoder_position_ids=SCREAMING_SNAKE_CASE_	,							)
					lowercase_					  = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]	,							dtype='''i4'''	)
					lowercase_					  = model.decode(
					    decoder_input_ids[:, -1:]	,							SCREAMING_SNAKE_CASE_	,							past_key_values=outputs_cache.past_key_values	,							decoder_attention_mask=SCREAMING_SNAKE_CASE_	,							decoder_position_ids=SCREAMING_SNAKE_CASE_	,							)

					lowercase_					  = model.decode(SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	,							decoder_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__(    unittest.TestCase							):



	"""simple docstring"""




	a      :Tuple   					=     99
	def 						_lowercase  (	self  :      Any	)	->					int:
					lowercase_					  = np.array(
					    [
					        [7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2],
					        [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2],
					        [5, 9_7, 1_7, 3_9, 9_4, 4_0, 2],
					        [7_6, 8_3, 9_4, 2_5, 7_0, 7_8, 2],
					        [8_7, 5_9, 4_1, 3_5, 4_8, 6_6, 2],
					        [5_5, 1_3, 1_6, 5_8, 5, 2, 1],  # note padding
					        [6_4, 2_7, 3_1, 5_1, 1_2, 7_5, 2],
					        [5_2, 6_4, 8_6, 1_7, 8_3, 3_9, 2],
					        [4_8, 6_1, 9, 2_4, 7_1, 8_2, 2],
					        [2_6, 1, 6_0, 4_8, 2_2, 1_3, 2],
					        [2_1, 5, 6_2, 2_8, 1_4, 7_6, 2],
					        [4_5, 9_8, 3_7, 8_6, 5_9, 4_8, 2],
					        [7_0, 7_0, 5_0, 9, 2_8, 0, 2],
					    ]	,							dtype=np.intaa	,							)

					lowercase_					  = input_ids.shape[0]
					lowercase_					  = MBartConfig(
					    vocab_size=self.vocab_size	,							d_model=2_4	,							encoder_layers=2	,							decoder_layers=2	,							encoder_attention_heads=2	,							decoder_attention_heads=2	,							encoder_ffn_dim=3_2	,							decoder_ffn_dim=3_2	,							max_position_embeddings=4_8	,							eos_token_id=2	,							pad_token_id=1	,							bos_token_id=0	,							)
					return config, input_ids, batch_size
	def 						_lowercase  (	self  :      Dict	)	->					Optional[Any]:
					lowercase_  ,     lowercase_  ,     lowercase_					  = self._get_config_and_data()
					lowercase_					  = FlaxMBartForSequenceClassification(SCREAMING_SNAKE_CASE_	)
					lowercase_					  = model(input_ids=SCREAMING_SNAKE_CASE_	,							decoder_input_ids=SCREAMING_SNAKE_CASE_	)
					lowercase_					  = (batch_size, config.num_labels)
					self.assertEqual(outputs['''logits'''].shape	,							SCREAMING_SNAKE_CASE_	)
	def 						_lowercase  (	self  :      str	)	->					str:
					lowercase_  ,     lowercase_  ,     lowercase_					  = self._get_config_and_data()
					lowercase_					  = FlaxMBartForQuestionAnswering(SCREAMING_SNAKE_CASE_	)
					lowercase_					  = model(input_ids=SCREAMING_SNAKE_CASE_	)

					self.assertEqual(outputs['''start_logits'''].shape	,							input_ids.shape	)
					self.assertEqual(outputs['''end_logits'''].shape	,							input_ids.shape	)
	def 						_lowercase  (	self  :      Any	)	->					List[str]:
					lowercase_  ,     lowercase_  ,     lowercase_					  = self._get_config_and_data()
					lowercase_					  = FlaxMBartForConditionalGeneration(SCREAMING_SNAKE_CASE_	)
					lowercase_					  = lm_model(input_ids=SCREAMING_SNAKE_CASE_	)
					lowercase_					  = (batch_size, input_ids.shape[1], config.vocab_size)
					self.assertEqual(outputs['''logits'''].shape	,							SCREAMING_SNAKE_CASE_	)
	def 						_lowercase  (	self  :      Any	)	->					int:
					lowercase_					  = MBartConfig(
					    vocab_size=self.vocab_size	,							d_model=1_4	,							encoder_layers=2	,							decoder_layers=2	,							encoder_attention_heads=2	,							decoder_attention_heads=2	,							encoder_ffn_dim=8	,							decoder_ffn_dim=8	,							max_position_embeddings=4_8	,							)
					lowercase_					  = FlaxMBartForConditionalGeneration(SCREAMING_SNAKE_CASE_	)
					lowercase_					  = np.array([[7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2], [6_8, 3_4, 2_6, 5_8, 3_0, 2, 1]]	,							dtype=np.intaa	)
					lowercase_					  = np.array([[8_2, 7_1, 8_2, 1_8, 2], [5_8, 6_8, 2, 1, 1]]	,							dtype=np.intaa	)
					lowercase_					  = lm_model(input_ids=SCREAMING_SNAKE_CASE_	,							decoder_input_ids=SCREAMING_SNAKE_CASE_	)
					lowercase_					  = (*summary.shape, config.vocab_size)
					self.assertEqual(outputs['''logits'''].shape	,							SCREAMING_SNAKE_CASE_	)




	def 						_lowercase  (	self  :      Optional[int]	)	->					Optional[Any]:
					lowercase_					  = np.array([[7_1, 8_2, 1_8, 3_3, 2, 1, 1], [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2]]	,							dtype=np.intaa	)
					lowercase_					  = shift_tokens_right(SCREAMING_SNAKE_CASE_	,							1	)
					lowercase_					  = np.equal(SCREAMING_SNAKE_CASE_	,							1	).astype(np.floataa	).sum()
					lowercase_					  = np.equal(SCREAMING_SNAKE_CASE_	,							1	).astype(np.floataa	).sum()
					self.assertEqual(shifted.shape	,							input_ids.shape	)
					self.assertEqual(SCREAMING_SNAKE_CASE_	,							n_pad_before - 1	)
					self.assertTrue(np.equal(shifted[:, 0]	,							2	).all()	)

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



	"""simple docstring"""




	a      :str   					=     True
	a      :Any   					=     (
	    (
	        FlaxMBartModel,
	        FlaxMBartForConditionalGeneration,
	        FlaxMBartForSequenceClassification,
	        FlaxMBartForQuestionAnswering,
	    )
	    if is_flax_available()
	    else ()
	)
	a      :int   					=     (FlaxMBartForConditionalGeneration,) if is_flax_available() else ()
	def 						_lowercase  (	self  :      int	)	->					Optional[int]:
					lowercase_					  = FlaxMBartModelTester(self	)
	def 						_lowercase  (	self  :      Optional[int]	)	->					List[Any]:
					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  :      Optional[int]	)	->					List[Any]:
					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_	)
	def 						_lowercase  (	self  :      int	)	->					Optional[Any]:
					lowercase_  ,     lowercase_					  = self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
									with self.subTest(model_class.__name__	):
													lowercase_					  = self._prepare_for_class(SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	)
													lowercase_					  = model_class(SCREAMING_SNAKE_CASE_	)

													@jax.jit
													def encode_jitted(SCREAMING_SNAKE_CASE_  :      Tuple	,							SCREAMING_SNAKE_CASE_  :      Tuple=None	,							**SCREAMING_SNAKE_CASE_  :      Tuple	):
																	return model.encode(input_ids=SCREAMING_SNAKE_CASE_	,							attention_mask=SCREAMING_SNAKE_CASE_	)

													with self.subTest('''JIT Enabled'''	):
																	lowercase_					  = encode_jitted(**SCREAMING_SNAKE_CASE_	).to_tuple()

													with self.subTest('''JIT Disabled'''	):
																	with jax.disable_jit():
																					lowercase_					  = encode_jitted(**SCREAMING_SNAKE_CASE_	).to_tuple()

													self.assertEqual(len(SCREAMING_SNAKE_CASE_	)	,							len(SCREAMING_SNAKE_CASE_	)	)
													for jitted_output, output in zip(SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	):
																	self.assertEqual(jitted_output.shape	,							output.shape	)
	def 						_lowercase  (	self  :      List[str]	)	->					int:
					lowercase_  ,     lowercase_					  = self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
									with self.subTest(model_class.__name__	):
													lowercase_					  = model_class(SCREAMING_SNAKE_CASE_	)
													lowercase_					  = model.encode(inputs_dict['''input_ids''']	,							inputs_dict['''attention_mask''']	)

													lowercase_					  = {
													    '''decoder_input_ids''': inputs_dict['''decoder_input_ids'''],
													    '''decoder_attention_mask''': inputs_dict['''decoder_attention_mask'''],
													    '''encoder_outputs''': encoder_outputs,
													}

													@jax.jit
													def decode_jitted(SCREAMING_SNAKE_CASE_  :      str	,							SCREAMING_SNAKE_CASE_  :      List[Any]	,							SCREAMING_SNAKE_CASE_  :      str	):
																	return model.decode(
																	    decoder_input_ids=SCREAMING_SNAKE_CASE_	,							decoder_attention_mask=SCREAMING_SNAKE_CASE_	,							encoder_outputs=SCREAMING_SNAKE_CASE_	,							)

													with self.subTest('''JIT Enabled'''	):
																	lowercase_					  = decode_jitted(**SCREAMING_SNAKE_CASE_	).to_tuple()

													with self.subTest('''JIT Disabled'''	):
																	with jax.disable_jit():
																					lowercase_					  = decode_jitted(**SCREAMING_SNAKE_CASE_	).to_tuple()

													self.assertEqual(len(SCREAMING_SNAKE_CASE_	)	,							len(SCREAMING_SNAKE_CASE_	)	)
													for jitted_output, output in zip(SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	):
																	self.assertEqual(jitted_output.shape	,							output.shape	)




	@slow
	def 						_lowercase  (	self  :      Any	)	->					int:
					for model_class_name in self.all_model_classes:
									lowercase_					  = model_class_name.from_pretrained('''facebook/mbart-large-cc25'''	,							from_pt=SCREAMING_SNAKE_CASE_	)
									# FlaxMBartForSequenceClassification expects eos token in input_ids
									lowercase_					  = np.ones((1, 1)	) * model.config.eos_token_id
									lowercase_					  = model(SCREAMING_SNAKE_CASE_	)
									self.assertIsNotNone(SCREAMING_SNAKE_CASE_	)

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



	"""simple docstring"""




	a      :Union[str, Any]   					=     [
	    ' UN Chief Says There Is No Military Solution in Syria',
	]
	a      :List[str]   					=     [
	    'Şeful ONU declară că nu există o soluţie militară în Siria',
	]
	a      :str   					=     'facebook/mbart-large-en-ro'
	@cached_property
	def 						_lowercase  (	self  :      Optional[Any]	)	->					Tuple:
					return AutoTokenizer.from_pretrained(self.model_name	)
	@cached_property
	def 						_lowercase  (	self  :      Any	)	->					Tuple:
					lowercase_					  = FlaxMBartForConditionalGeneration.from_pretrained(self.model_name	,							from_pt=SCREAMING_SNAKE_CASE_	)
					return model
	def 						_lowercase  (	self  :      List[str]	,							**SCREAMING_SNAKE_CASE_  :      str	)	->					Optional[Any]:
					lowercase_					  = self.translate_src_text(**SCREAMING_SNAKE_CASE_	)
					self.assertListEqual(self.expected_text	,							SCREAMING_SNAKE_CASE_	)
	def 						_lowercase  (	self  :      Tuple	,							**SCREAMING_SNAKE_CASE_  :      Union[str, Any]	)	->					Dict:
					lowercase_					  = self.tokenizer(self.src_text	,							**SCREAMING_SNAKE_CASE_	,							return_tensors='''np'''	)
					lowercase_					  = self.model.generate(
					    model_inputs.input_ids	,							attention_mask=model_inputs.attention_mask	,							decoder_start_token_id=self.tokenizer.lang_code_to_id['''ro_RO''']	,							early_stopping=SCREAMING_SNAKE_CASE_	,							num_beams=2	,							).sequences
					lowercase_					  = self.tokenizer.batch_decode(SCREAMING_SNAKE_CASE_	,							skip_special_tokens=SCREAMING_SNAKE_CASE_	)
					return generated_words




	@slow
	def 						_lowercase  (	self  :      Dict	)	->					List[Any]:
					self._assert_generated_batch_equal_expected()






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	)




