"""simple docstring"""







import argparse

import torch

from transformers import RoFormerConfig, RoFormerForMaskedLM, load_tf_weights_in_roformer
from transformers.utils import logging


logging.set_verbosity_info()


def 	a__   (		UpperCAmelCase__							:      Optional[int]		,      UpperCAmelCase__							:      int		,      UpperCAmelCase__							:      str      )->						Union[str, Any]:
						# Initialise PyTorch model
						UpperCAmelCase__       						=		RoFormerConfig.from_json_file(UpperCAmelCase__      )
						print(f'Building PyTorch model from configuration: {config}'      )
						UpperCAmelCase__       						=		RoFormerForMaskedLM(UpperCAmelCase__      )

						# Load weights from tf checkpoint
						load_tf_weights_in_roformer(UpperCAmelCase__		,      UpperCAmelCase__		,      UpperCAmelCase__      )

						# Save pytorch-model
						print(f'Save PyTorch model to {pytorch_dump_path}'      )
						torch.save(model.state_dict()		,      UpperCAmelCase__		,      _use_new_zipfile_serialization=UpperCAmelCase__      )


if __name__ == "__main__":
						UpperCamelCase_							:							str				 =		argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
						)
						parser.add_argument(
						    """--bert_config_file""",
						    default=None,
						    type=str,
						    required=True,
						    help=(
						        """The config json file corresponding to the pre-trained BERT model. \n"""
						        """This specifies the model architecture."""
						    ),
						)
						parser.add_argument(
						    """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
						)
						UpperCamelCase_							:							Union[str, Any]				 =		parser.parse_args()
						convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)


"""simple docstring"""







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
						UpperCamelCase_							:							Union[str, Any]				 =		"""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__   (		UpperCAmelCase__							:      str		,      UpperCAmelCase__							:      List[Any]		,      UpperCAmelCase__							:      Union[str, Any]=None		,      UpperCAmelCase__							:      Optional[Any]=None		,      UpperCAmelCase__							:      Dict=None		,      UpperCAmelCase__							:      str=None		,      UpperCAmelCase__							:      int=None		,      UpperCAmelCase__							:      Optional[Any]=None		,      )->						Dict:
						if attention_mask is None:
												UpperCAmelCase__       						=		np.where(input_ids != config.pad_token_id		,      1		,      0      )
						if decoder_attention_mask is None:
												UpperCAmelCase__       						=		np.where(decoder_input_ids != config.pad_token_id		,      1		,      0      )
						if head_mask is None:
												UpperCAmelCase__       						=		np.ones((config.encoder_layers, config.encoder_attention_heads)      )
						if decoder_head_mask is None:
												UpperCAmelCase__       						=		np.ones((config.decoder_layers, config.decoder_attention_heads)      )
						if cross_attn_head_mask is None:
												UpperCAmelCase__       						=		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	):

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


										'''simple docstring'''
										UpperCAmelCase__       						=		parent
										UpperCAmelCase__       						=		batch_size
										UpperCAmelCase__       						=		seq_length
										UpperCAmelCase__       						=		is_training
										UpperCAmelCase__       						=		use_labels
										UpperCAmelCase__       						=		vocab_size
										UpperCAmelCase__       						=		hidden_size
										UpperCAmelCase__       						=		num_hidden_layers
										UpperCAmelCase__       						=		num_attention_heads
										UpperCAmelCase__       						=		intermediate_size
										UpperCAmelCase__       						=		hidden_act
										UpperCAmelCase__       						=		hidden_dropout_prob
										UpperCAmelCase__       						=		attention_probs_dropout_prob
										UpperCAmelCase__       						=		max_position_embeddings
										UpperCAmelCase__       						=		eos_token_id
										UpperCAmelCase__       						=		pad_token_id
										UpperCAmelCase__       						=		bos_token_id
										UpperCAmelCase__       						=		decoder_start_token_id
										UpperCAmelCase__       						=		initializer_range

				def 							A			(  self							:     Dict  ):


										'''simple docstring'''
										UpperCAmelCase__       						=		np.clip(ids_tensor([self.batch_size, self.seq_length - 1]							,     self.vocab_size  )							,     3							,     self.vocab_size  )
										UpperCAmelCase__       						=		np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)							,     dtype=np.intaa  ))							,     -1  )

										UpperCAmelCase__       						=		shift_tokens_right(_lowercase							,     1  )

										UpperCAmelCase__       						=		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=_lowercase							,     )
										UpperCAmelCase__       						=		prepare_mbart_inputs_dict(_lowercase							,     _lowercase							,     _lowercase  )
										return config, inputs_dict

				def 							A			(  self							:     Tuple  ):


										'''simple docstring'''
										UpperCAmelCase__						,   UpperCAmelCase__       						=		self.prepare_config_and_inputs()
										return config, inputs_dict

				def 							A			(  self							:     Any							,     _lowercase							:     List[Any]							,     _lowercase							:     Any							,     _lowercase							:     Optional[Any]  ):


										'''simple docstring'''
										UpperCAmelCase__       						=		2_0
										UpperCAmelCase__       						=		model_class_name(_lowercase  )

										UpperCAmelCase__       						=		model.encode(inputs_dict["""input_ids"""]  )

										UpperCAmelCase__						,   UpperCAmelCase__       						=		(
										    inputs_dict["""decoder_input_ids"""],
										    inputs_dict["""decoder_attention_mask"""],
										)

										UpperCAmelCase__       						=		model.init_cache(decoder_input_ids.shape[0]							,     _lowercase							,     _lowercase  )
										UpperCAmelCase__       						=		jnp.ones((decoder_input_ids.shape[0], max_decoder_length)							,     dtype="""i4"""  )

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

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

										UpperCAmelCase__       						=		model.decode(_lowercase							,     _lowercase  )

										UpperCAmelCase__       						=		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 							A			(  self							:     Tuple							,     _lowercase							:     List[str]							,     _lowercase							:     List[Any]							,     _lowercase							:     List[Any]  ):


										'''simple docstring'''
										UpperCAmelCase__       						=		2_0
										UpperCAmelCase__       						=		model_class_name(_lowercase  )

										UpperCAmelCase__       						=		model.encode(inputs_dict["""input_ids"""]  )

										UpperCAmelCase__						,   UpperCAmelCase__       						=		(
										    inputs_dict["""decoder_input_ids"""],
										    inputs_dict["""decoder_attention_mask"""],
										)

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

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

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

										UpperCAmelCase__       						=		model.decode(_lowercase							,     _lowercase							,     decoder_attention_mask=_lowercase  )

										UpperCAmelCase__       						=		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	):
				lowerCamelCase__							=							9_9

				def 							A			(  self							:     str  ):


										'''simple docstring'''
										UpperCAmelCase__       						=		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							,     )

										UpperCAmelCase__       						=		input_ids.shape[0]
										UpperCAmelCase__       						=		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 							A			(  self							:     Optional[int]  ):


										'''simple docstring'''
										UpperCAmelCase__						,   UpperCAmelCase__						,   UpperCAmelCase__       						=		self._get_config_and_data()
										UpperCAmelCase__       						=		FlaxMBartForSequenceClassification(_lowercase  )
										UpperCAmelCase__       						=		model(input_ids=_lowercase							,     decoder_input_ids=_lowercase  )
										UpperCAmelCase__       						=		(batch_size, config.num_labels)
										self.assertEqual(outputs["""logits"""].shape							,     _lowercase  )

				def 							A			(  self							:     Optional[int]  ):


										'''simple docstring'''
										UpperCAmelCase__						,   UpperCAmelCase__						,   UpperCAmelCase__       						=		self._get_config_and_data()
										UpperCAmelCase__       						=		FlaxMBartForQuestionAnswering(_lowercase  )
										UpperCAmelCase__       						=		model(input_ids=_lowercase  )

										self.assertEqual(outputs["""start_logits"""].shape							,     input_ids.shape  )
										self.assertEqual(outputs["""end_logits"""].shape							,     input_ids.shape  )

				def 							A			(  self							:     Any  ):


										'''simple docstring'''
										UpperCAmelCase__						,   UpperCAmelCase__						,   UpperCAmelCase__       						=		self._get_config_and_data()
										UpperCAmelCase__       						=		FlaxMBartForConditionalGeneration(_lowercase  )
										UpperCAmelCase__       						=		lm_model(input_ids=_lowercase  )
										UpperCAmelCase__       						=		(batch_size, input_ids.shape[1], config.vocab_size)
										self.assertEqual(outputs["""logits"""].shape							,     _lowercase  )

				def 							A			(  self							:     Optional[int]  ):


										'''simple docstring'''
										UpperCAmelCase__       						=		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							,     )
										UpperCAmelCase__       						=		FlaxMBartForConditionalGeneration(_lowercase  )
										UpperCAmelCase__       						=		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  )
										UpperCAmelCase__       						=		np.array([[8_2, 7_1, 8_2, 1_8, 2], [5_8, 6_8, 2, 1, 1]]							,     dtype=np.intaa  )
										UpperCAmelCase__       						=		lm_model(input_ids=_lowercase							,     decoder_input_ids=_lowercase  )
										UpperCAmelCase__       						=		(*summary.shape, config.vocab_size)
										self.assertEqual(outputs["""logits"""].shape							,     _lowercase  )

				def 							A			(  self							:     List[str]  ):


										'''simple docstring'''
										UpperCAmelCase__       						=		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  )
										UpperCAmelCase__       						=		shift_tokens_right(_lowercase							,     1  )
										UpperCAmelCase__       						=		np.equal(_lowercase							,     1  ).astype(np.floataa  ).sum()
										UpperCAmelCase__       						=		np.equal(_lowercase							,     1  ).astype(np.floataa  ).sum()
										self.assertEqual(shifted.shape							,     input_ids.shape  )
										self.assertEqual(_lowercase							,     n_pad_before - 1  )
										self.assertTrue(np.equal(shifted[:, 0]							,     2  ).all()  )


@require_flax
class       _lowercase		( __lowerCamelCase   ,   unittest.TestCase   ,   __lowerCamelCase	):
				lowerCamelCase__							=							True
				lowerCamelCase__							=							(
				    (
				        FlaxMBartModel,
				        FlaxMBartForConditionalGeneration,
				        FlaxMBartForSequenceClassification,
				        FlaxMBartForQuestionAnswering,
				    )
				    if is_flax_available()
				    else ()
				)
				lowerCamelCase__							=							(FlaxMBartForConditionalGeneration,) if is_flax_available() else ()

				def 							A			(  self							:     List[str]  ):


										'''simple docstring'''
										UpperCAmelCase__       						=		FlaxMBartModelTester(self  )

				def 							A			(  self							:     Dict  ):


										'''simple docstring'''
										UpperCAmelCase__						,   UpperCAmelCase__       						=		self.model_tester.prepare_config_and_inputs()
										for model_class in self.all_model_classes:
																self.model_tester.check_use_cache_forward(_lowercase							,     _lowercase							,     _lowercase  )

				def 							A			(  self							:     int  ):


										'''simple docstring'''
										UpperCAmelCase__						,   UpperCAmelCase__       						=		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(_lowercase							,     _lowercase							,     _lowercase  )

				def 							A			(  self							:     Any  ):


										'''simple docstring'''
										UpperCAmelCase__						,   UpperCAmelCase__       						=		self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																with self.subTest(model_class.__name__  ):
																						UpperCAmelCase__       						=		self._prepare_for_class(_lowercase							,     _lowercase  )
																						UpperCAmelCase__       						=		model_class(_lowercase  )

																						@jax.jit
																						def encode_jitted(_lowercase							:     str							,     _lowercase							:     Optional[Any]=None							,     **_lowercase							:     Any  ):
																												return model.encode(input_ids=_lowercase							,     attention_mask=_lowercase  )

																						with self.subTest("""JIT Enabled"""  ):
																												UpperCAmelCase__       						=		encode_jitted(**_lowercase  ).to_tuple()

																						with self.subTest("""JIT Disabled"""  ):
																												with jax.disable_jit():
																																		UpperCAmelCase__       						=		encode_jitted(**_lowercase  ).to_tuple()

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

				def 							A			(  self							:     List[str]  ):


										'''simple docstring'''
										UpperCAmelCase__						,   UpperCAmelCase__       						=		self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																with self.subTest(model_class.__name__  ):
																						UpperCAmelCase__       						=		model_class(_lowercase  )
																						UpperCAmelCase__       						=		model.encode(inputs_dict["""input_ids"""]							,     inputs_dict["""attention_mask"""]  )

																						UpperCAmelCase__       						=		{
																						    """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(_lowercase							:     Union[str, Any]							,     _lowercase							:     str							,     _lowercase							:     Optional[Any]  ):
																												return model.decode(
																												    decoder_input_ids=_lowercase							,     decoder_attention_mask=_lowercase							,     encoder_outputs=_lowercase							,     )

																						with self.subTest("""JIT Enabled"""  ):
																												UpperCAmelCase__       						=		decode_jitted(**_lowercase  ).to_tuple()

																						with self.subTest("""JIT Disabled"""  ):
																												with jax.disable_jit():
																																		UpperCAmelCase__       						=		decode_jitted(**_lowercase  ).to_tuple()

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

				@slow
				def 							A			(  self							:     Dict  ):


										'''simple docstring'''
										for model_class_name in self.all_model_classes:
																UpperCAmelCase__       						=		model_class_name.from_pretrained("""facebook/mbart-large-cc25"""							,     from_pt=_lowercase  )
																# FlaxMBartForSequenceClassification expects eos token in input_ids
																UpperCAmelCase__       						=		np.ones((1, 1)  ) * model.config.eos_token_id
																UpperCAmelCase__       						=		model(_lowercase  )
																self.assertIsNotNone(_lowercase  )


@require_flax
@require_sentencepiece
@require_tokenizers
class       _lowercase		( unittest.TestCase	):
				lowerCamelCase__							=							[
				    ' UN Chief Says There Is No Military Solution in Syria',
				]
				lowerCamelCase__							=							[
				    'Şeful ONU declară că nu există o soluţie militară în Siria',
				]
				lowerCamelCase__							=							'facebook/mbart-large-en-ro'

				@cached_property
				def 							A			(  self							:     int  ):


										'''simple docstring'''
										return AutoTokenizer.from_pretrained(self.model_name  )

				@cached_property
				def 							A			(  self							:     Optional[int]  ):


										'''simple docstring'''
										UpperCAmelCase__       						=		FlaxMBartForConditionalGeneration.from_pretrained(self.model_name							,     from_pt=_lowercase  )
										return model

				def 							A			(  self							:     Optional[int]							,     **_lowercase							:     str  ):


										'''simple docstring'''
										UpperCAmelCase__       						=		self.translate_src_text(**_lowercase  )
										self.assertListEqual(self.expected_text							,     _lowercase  )

				def 							A			(  self							:     Optional[Any]							,     **_lowercase							:     Optional[Any]  ):


										'''simple docstring'''
										UpperCAmelCase__       						=		self.tokenizer(self.src_text							,     **_lowercase							,     return_tensors="""np"""  )
										UpperCAmelCase__       						=		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=_lowercase							,     num_beams=2							,     ).sequences
										UpperCAmelCase__       						=		self.tokenizer.batch_decode(_lowercase							,     skip_special_tokens=_lowercase  )
										return generated_words

				@slow
				def 							A			(  self							:     Dict  ):


										'''simple docstring'''
										self._assert_generated_batch_equal_expected()

