

'''simple docstring'''





import argparse
import pathlib

import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import RobertaConfig, RobertaForMaskedLM, RobertaForSequenceClassification
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse("""0.9.0"""):
						raise Exception("""requires fairseq >= 0.9.0""")


logging.set_verbosity_info()
lowerCAmelCase							:						int     			=		logging.get_logger(__name__)

lowerCAmelCase							:						Dict     			=		"""Hello world! cécé herlolip"""







def     A_   (					_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase					):
						SCREAMING_SNAKE_CASE_:				Union[str, Any]							= FairseqRobertaModel.from_pretrained(_UpperCAmelCase					)
						roberta.eval()  # disable dropout
						SCREAMING_SNAKE_CASE_:				Optional[int]							= roberta.model.encoder.sentence_encoder
						SCREAMING_SNAKE_CASE_:				Union[str, Any]							= RobertaConfig(
						    vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings       ,			hidden_size=roberta.args.encoder_embed_dim       ,			num_hidden_layers=roberta.args.encoder_layers       ,			num_attention_heads=roberta.args.encoder_attention_heads       ,			intermediate_size=roberta.args.encoder_ffn_embed_dim       ,			max_position_embeddings=5_14       ,			type_vocab_size=1       ,			layer_norm_eps=1e-5       ,			)
						if classification_head:
												SCREAMING_SNAKE_CASE_:				Tuple							= roberta.model.classification_heads["mnli"].out_proj.weight.shape[0]
						print("Our BERT config:"       ,			_UpperCAmelCase					)

						SCREAMING_SNAKE_CASE_:				Any							= RobertaForSequenceClassification(_UpperCAmelCase					) if classification_head else RobertaForMaskedLM(_UpperCAmelCase					)
						model.eval()

						# Now let's copy all the weights.
						# Embeddings
						SCREAMING_SNAKE_CASE_:				int							= roberta_sent_encoder.embed_tokens.weight
						SCREAMING_SNAKE_CASE_:				int							= roberta_sent_encoder.embed_positions.weight
						SCREAMING_SNAKE_CASE_:				int							= torch.zeros_like(
						    model.roberta.embeddings.token_type_embeddings.weight					)  # just zero them out b/c RoBERTa doesn't use them.
						SCREAMING_SNAKE_CASE_:				Optional[Any]							= roberta_sent_encoder.emb_layer_norm.weight
						SCREAMING_SNAKE_CASE_:				Tuple							= roberta_sent_encoder.emb_layer_norm.bias

						for i in range(config.num_hidden_layers					):
												# Encoder: start of layer
												SCREAMING_SNAKE_CASE_:				BertLayer							= model.roberta.encoder.layer[i]
												SCREAMING_SNAKE_CASE_:				TransformerSentenceEncoderLayer							= roberta_sent_encoder.layers[i]

												# self attention
												SCREAMING_SNAKE_CASE_:				BertSelfAttention							= layer.attention.self
												assert (
												    roberta_layer.self_attn.k_proj.weight.data.shape
												    == roberta_layer.self_attn.q_proj.weight.data.shape
												    == roberta_layer.self_attn.v_proj.weight.data.shape
												    == torch.Size((config.hidden_size, config.hidden_size)					)
												)

												SCREAMING_SNAKE_CASE_:				Optional[int]							= roberta_layer.self_attn.q_proj.weight
												SCREAMING_SNAKE_CASE_:				Any							= roberta_layer.self_attn.q_proj.bias
												SCREAMING_SNAKE_CASE_:				Dict							= roberta_layer.self_attn.k_proj.weight
												SCREAMING_SNAKE_CASE_:				Union[str, Any]							= roberta_layer.self_attn.k_proj.bias
												SCREAMING_SNAKE_CASE_:				Tuple							= roberta_layer.self_attn.v_proj.weight
												SCREAMING_SNAKE_CASE_:				Tuple							= roberta_layer.self_attn.v_proj.bias

												# self-attention output
												SCREAMING_SNAKE_CASE_:				BertSelfOutput							= layer.attention.output
												assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
												SCREAMING_SNAKE_CASE_:				List[Any]							= roberta_layer.self_attn.out_proj.weight
												SCREAMING_SNAKE_CASE_:				Dict							= roberta_layer.self_attn.out_proj.bias
												SCREAMING_SNAKE_CASE_:				Dict							= roberta_layer.self_attn_layer_norm.weight
												SCREAMING_SNAKE_CASE_:				int							= roberta_layer.self_attn_layer_norm.bias

												# intermediate
												SCREAMING_SNAKE_CASE_:				BertIntermediate							= layer.intermediate
												assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
												SCREAMING_SNAKE_CASE_:				str							= roberta_layer.fca.weight
												SCREAMING_SNAKE_CASE_:				List[Any]							= roberta_layer.fca.bias

												# output
												SCREAMING_SNAKE_CASE_:				BertOutput							= layer.output
												assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
												SCREAMING_SNAKE_CASE_:				Tuple							= roberta_layer.fca.weight
												SCREAMING_SNAKE_CASE_:				Optional[Any]							= roberta_layer.fca.bias
												SCREAMING_SNAKE_CASE_:				Union[str, Any]							= roberta_layer.final_layer_norm.weight
												SCREAMING_SNAKE_CASE_:				int							= roberta_layer.final_layer_norm.bias
												# end of layer

						if classification_head:
												SCREAMING_SNAKE_CASE_:				List[str]							= roberta.model.classification_heads["mnli"].dense.weight
												SCREAMING_SNAKE_CASE_:				Dict							= roberta.model.classification_heads["mnli"].dense.bias
												SCREAMING_SNAKE_CASE_:				Any							= roberta.model.classification_heads["mnli"].out_proj.weight
												SCREAMING_SNAKE_CASE_:				Tuple							= roberta.model.classification_heads["mnli"].out_proj.bias
						else:
												# LM Head
												SCREAMING_SNAKE_CASE_:				Optional[Any]							= roberta.model.encoder.lm_head.dense.weight
												SCREAMING_SNAKE_CASE_:				Union[str, Any]							= roberta.model.encoder.lm_head.dense.bias
												SCREAMING_SNAKE_CASE_:				int							= roberta.model.encoder.lm_head.layer_norm.weight
												SCREAMING_SNAKE_CASE_:				Optional[Any]							= roberta.model.encoder.lm_head.layer_norm.bias
												SCREAMING_SNAKE_CASE_:				List[str]							= roberta.model.encoder.lm_head.weight
												SCREAMING_SNAKE_CASE_:				int							= roberta.model.encoder.lm_head.bias

						# Let's check that we get the same results.
						SCREAMING_SNAKE_CASE_:				torch.Tensor							= roberta.encode(_UpperCAmelCase					).unsqueeze(0					)  # batch of size 1

						SCREAMING_SNAKE_CASE_:				List[Any]							= model(_UpperCAmelCase					)[0]
						if classification_head:
												SCREAMING_SNAKE_CASE_:				Optional[int]							= roberta.model.classification_heads["mnli"](roberta.extract_features(_UpperCAmelCase					)					)
						else:
												SCREAMING_SNAKE_CASE_:				Optional[Any]							= roberta.model(_UpperCAmelCase					)[0]
						print(our_output.shape       ,			their_output.shape					)
						SCREAMING_SNAKE_CASE_:				Optional[Any]							= torch.max(torch.abs(our_output - their_output					)					).item()
						print(f"max_absolute_diff = {max_absolute_diff}"					)  # ~ 1e-7
						SCREAMING_SNAKE_CASE_:				List[str]							= torch.allclose(_UpperCAmelCase       ,			_UpperCAmelCase       ,			atol=1e-3					)
						print("Do both models output the same tensors?"       ,			"🔥" if success else "💩"					)
						if not success:
												raise Exception("Something went wRoNg"					)

						pathlib.Path(_UpperCAmelCase					).mkdir(parents=_UpperCAmelCase       ,			exist_ok=_UpperCAmelCase					)
						print(f"Saving model to {pytorch_dump_folder_path}"					)
						model.save_pretrained(_UpperCAmelCase					)


if __name__ == "__main__":
						lowerCAmelCase							:						List[Any]     			=		argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    """--roberta_checkpoint_path""", default=None, type=str, required=True, help="""Path the official PyTorch dump."""
						)
						parser.add_argument(
						    """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
						)
						parser.add_argument(
						    """--classification_head""", action="""store_true""", help="""Whether to convert a final classification head."""
						)
						lowerCAmelCase							:						Tuple     			=		parser.parse_args()
						convert_roberta_checkpoint_to_pytorch(
						    args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
						)









import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaConfig, GPTaTokenizer, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, slow

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


if is_flax_available():
						import jax
						import jax.numpy as jnp

						from transformers.modeling_flax_pytorch_utils import (
						    convert_pytorch_state_dict_to_flax,
						    load_flax_weights_in_pytorch_model,
						)
						from transformers.models.gpta.modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel

if is_torch_available():
						import torch



class 						__lowercase       :




			"""simple docstring"""







			def __init__(				self						:  Optional[Any]     ,		lowerCAmelCase__						:  Any     ,		lowerCAmelCase__						:  Optional[Any]=14     ,		lowerCAmelCase__						:  List[Any]=7     ,		lowerCAmelCase__						:  List[str]=True     ,		lowerCAmelCase__						:  int=True     ,		lowerCAmelCase__						:  str=False     ,		lowerCAmelCase__						:  List[str]=True     ,		lowerCAmelCase__						:  int=99     ,		lowerCAmelCase__						:  List[str]=32     ,		lowerCAmelCase__						:  Dict=5     ,		lowerCAmelCase__						:  Union[str, Any]=4     ,		lowerCAmelCase__						:  List[Any]=37     ,		lowerCAmelCase__						:  int="gelu"     ,		lowerCAmelCase__						:  List[Any]=0.1     ,		lowerCAmelCase__						:  Optional[int]=0.1     ,		lowerCAmelCase__						:  Optional[int]=512     ,		lowerCAmelCase__						:  Any=0.02     ,		):
									SCREAMING_SNAKE_CASE_:				Tuple							= parent
									SCREAMING_SNAKE_CASE_:				Optional[Any]							= batch_size
									SCREAMING_SNAKE_CASE_:				Tuple							= seq_length
									SCREAMING_SNAKE_CASE_:				Dict							= is_training
									SCREAMING_SNAKE_CASE_:				Dict							= use_input_mask
									SCREAMING_SNAKE_CASE_:				Any							= use_token_type_ids
									SCREAMING_SNAKE_CASE_:				Dict							= use_labels
									SCREAMING_SNAKE_CASE_:				List[str]							= vocab_size
									SCREAMING_SNAKE_CASE_:				str							= hidden_size
									SCREAMING_SNAKE_CASE_:				List[Any]							= num_hidden_layers
									SCREAMING_SNAKE_CASE_:				Optional[int]							= num_attention_heads
									SCREAMING_SNAKE_CASE_:				Any							= intermediate_size
									SCREAMING_SNAKE_CASE_:				Union[str, Any]							= hidden_act
									SCREAMING_SNAKE_CASE_:				List[Any]							= hidden_dropout_prob
									SCREAMING_SNAKE_CASE_:				Dict							= attention_probs_dropout_prob
									SCREAMING_SNAKE_CASE_:				Dict							= max_position_embeddings
									SCREAMING_SNAKE_CASE_:				List[str]							= initializer_range
									SCREAMING_SNAKE_CASE_:				Any							= None
									SCREAMING_SNAKE_CASE_:				int							= vocab_size - 1
									SCREAMING_SNAKE_CASE_:				List[Any]							= vocab_size - 1
									SCREAMING_SNAKE_CASE_:				Any							= vocab_size - 1



			def  _SCREAMING_SNAKE_CASE    (				self						:  Optional[Any]):
									SCREAMING_SNAKE_CASE_:				Union[str, Any]							= ids_tensor([self.batch_size, self.seq_length]     ,		self.vocab_size)

									SCREAMING_SNAKE_CASE_:				Any							= None
									if self.use_input_mask:
															SCREAMING_SNAKE_CASE_:				str							= random_attention_mask([self.batch_size, self.seq_length])

									SCREAMING_SNAKE_CASE_:				Any							= GPTaConfig(
									    vocab_size=self.vocab_size     ,		n_embd=self.hidden_size     ,		n_layer=self.num_hidden_layers     ,		n_head=self.num_attention_heads     ,		n_positions=self.max_position_embeddings     ,		use_cache=lowerCAmelCase__     ,		bos_token_id=self.bos_token_id     ,		eos_token_id=self.eos_token_id     ,		pad_token_id=self.pad_token_id     ,		)

									return (config, input_ids, input_mask)



			def  _SCREAMING_SNAKE_CASE    (				self						:  int):
									SCREAMING_SNAKE_CASE_:				int							= self.prepare_config_and_inputs()
									SCREAMING_SNAKE_CASE_	,					SCREAMING_SNAKE_CASE_	,					SCREAMING_SNAKE_CASE_:				Tuple							= config_and_inputs
									SCREAMING_SNAKE_CASE_:				Optional[Any]							= {"input_ids": input_ids, "attention_mask": attention_mask}
									return config, inputs_dict



			def  _SCREAMING_SNAKE_CASE    (				self						:  List[Any]):
									SCREAMING_SNAKE_CASE_	,					SCREAMING_SNAKE_CASE_	,					SCREAMING_SNAKE_CASE_:				str							= self.prepare_config_and_inputs()

									SCREAMING_SNAKE_CASE_:				List[Any]							= floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
									SCREAMING_SNAKE_CASE_:				List[str]							= ids_tensor([self.batch_size, self.seq_length]     ,		vocab_size=2)

									return (
									    config,
									    input_ids,
									    attention_mask,
									    encoder_hidden_states,
									    encoder_attention_mask,
									)



			def  _SCREAMING_SNAKE_CASE    (				self						:  List[str]     ,		lowerCAmelCase__						:  Optional[int]     ,		lowerCAmelCase__						:  List[str]     ,		lowerCAmelCase__						:  Optional[int]     ,		lowerCAmelCase__						:  Union[str, Any]):
									SCREAMING_SNAKE_CASE_:				Dict							= 20
									SCREAMING_SNAKE_CASE_:				Optional[int]							= model_class_name(lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:				Union[str, Any]							= model.init_cache(input_ids.shape[0]     ,		lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:				Optional[int]							= jnp.ones((input_ids.shape[0], max_decoder_length)     ,		dtype="i4")

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

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

									SCREAMING_SNAKE_CASE_:				Tuple							= model(lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:				str							= 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  _SCREAMING_SNAKE_CASE    (				self						:  List[str]     ,		lowerCAmelCase__						:  Optional[Any]     ,		lowerCAmelCase__						:  str     ,		lowerCAmelCase__						:  Optional[int]     ,		lowerCAmelCase__						:  Any):
									SCREAMING_SNAKE_CASE_:				Any							= 20
									SCREAMING_SNAKE_CASE_:				Union[str, Any]							= model_class_name(lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:				List[Any]							= jnp.concatenate(
									    [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1]))]     ,		axis=-1     ,		)

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

									SCREAMING_SNAKE_CASE_:				Union[str, Any]							= model(
									    input_ids[:, :-1]     ,		attention_mask=lowerCAmelCase__     ,		past_key_values=lowerCAmelCase__     ,		position_ids=lowerCAmelCase__     ,		)
									SCREAMING_SNAKE_CASE_:				int							= jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]     ,		dtype="i4")
									SCREAMING_SNAKE_CASE_:				Optional[Any]							= model(
									    input_ids[:, -1:]     ,		past_key_values=outputs_cache.past_key_values     ,		attention_mask=lowerCAmelCase__     ,		position_ids=lowerCAmelCase__     ,		)

									SCREAMING_SNAKE_CASE_:				Any							= model(lowerCAmelCase__     ,		attention_mask=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:				List[Any]							= 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_	, UpperCAmelCase_	, unittest.TestCase			):




			"""simple docstring"""







			_UpperCAmelCase     :							str    							=  (FlaxGPTaModel, FlaxGPTaLMHeadModel) if is_flax_available() else ()
			_UpperCAmelCase     :							Optional[Any]    							=  (FlaxGPTaLMHeadModel,) if is_flax_available() else ()



			def  _SCREAMING_SNAKE_CASE    (				self						:  str):
									SCREAMING_SNAKE_CASE_:				Tuple							= FlaxGPTaModelTester(self)



			def  _SCREAMING_SNAKE_CASE    (				self						:  Union[str, Any]):
									for model_class_name in self.all_model_classes:
															SCREAMING_SNAKE_CASE_	,					SCREAMING_SNAKE_CASE_	,					SCREAMING_SNAKE_CASE_:				List[str]							= self.model_tester.prepare_config_and_inputs()
															self.model_tester.check_use_cache_forward(lowerCAmelCase__     ,		lowerCAmelCase__     ,		lowerCAmelCase__     ,		lowerCAmelCase__)



			def  _SCREAMING_SNAKE_CASE    (				self						:  Optional[Any]):
									for model_class_name in self.all_model_classes:
															SCREAMING_SNAKE_CASE_	,					SCREAMING_SNAKE_CASE_	,					SCREAMING_SNAKE_CASE_:				Optional[int]							= self.model_tester.prepare_config_and_inputs()
															self.model_tester.check_use_cache_forward_with_attn_mask(
															    lowerCAmelCase__     ,		lowerCAmelCase__     ,		lowerCAmelCase__     ,		lowerCAmelCase__)



			@slow
			def  _SCREAMING_SNAKE_CASE    (				self						:  Dict):
									SCREAMING_SNAKE_CASE_:				Optional[Any]							= GPTaTokenizer.from_pretrained("gpt2"     ,		pad_token="</s>"     ,		padding_side="left")
									SCREAMING_SNAKE_CASE_:				Any							= tokenizer(["Hello this is a long string", "Hey"]     ,		return_tensors="np"     ,		padding=lowerCAmelCase__     ,		truncation=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:				str							= FlaxGPTaLMHeadModel.from_pretrained("gpt2")
									SCREAMING_SNAKE_CASE_:				Dict							= False
									SCREAMING_SNAKE_CASE_:				Dict							= model.config.eos_token_id

									SCREAMING_SNAKE_CASE_:				str							= jax.jit(model.generate)

									SCREAMING_SNAKE_CASE_:				Union[str, Any]							= jit_generate(inputs["input_ids"]     ,		attention_mask=inputs["attention_mask"]).sequences

									SCREAMING_SNAKE_CASE_:				Dict							= tokenizer.batch_decode(lowerCAmelCase__     ,		skip_special_tokens=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:				Any							= [
									    "Hello this is a long string of words. I'm going to start with the first one.\n",
									    "Hey, I'm not sure if I'm going to be able to do",
									]

									self.assertListEqual(lowerCAmelCase__     ,		lowerCAmelCase__)



			@is_pt_flax_cross_test
			def  _SCREAMING_SNAKE_CASE    (				self						:  int):
									SCREAMING_SNAKE_CASE_	,					SCREAMING_SNAKE_CASE_:				int							= self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
															with self.subTest(model_class.__name__):
																					# prepare inputs
																					SCREAMING_SNAKE_CASE_:				List[str]							= self._prepare_for_class(lowerCAmelCase__     ,		lowerCAmelCase__)
																					SCREAMING_SNAKE_CASE_:				List[str]							= {k: torch.tensor(v.tolist()) for k, v in prepared_inputs_dict.items()}

																					# load corresponding PyTorch class
																					SCREAMING_SNAKE_CASE_:				List[str]							= model_class.__name__[4:]  # Skip the "Flax" at the beginning
																					SCREAMING_SNAKE_CASE_:				str							= getattr(lowerCAmelCase__     ,		lowerCAmelCase__)

																					SCREAMING_SNAKE_CASE_	,					SCREAMING_SNAKE_CASE_:				List[str]							= pt_inputs["input_ids"].shape
																					SCREAMING_SNAKE_CASE_:				List[str]							= np.random.randint(0     ,		seq_length - 1     ,		size=(batch_size,))
																					for batch_idx, start_index in enumerate(lowerCAmelCase__):
																											SCREAMING_SNAKE_CASE_:				Any							= 0
																											SCREAMING_SNAKE_CASE_:				int							= 1
																											SCREAMING_SNAKE_CASE_:				List[Any]							= 0
																											SCREAMING_SNAKE_CASE_:				str							= 1
																					SCREAMING_SNAKE_CASE_:				List[Any]							= pt_model_class(lowerCAmelCase__).eval()
																					SCREAMING_SNAKE_CASE_:				Tuple							= model_class(lowerCAmelCase__     ,		dtype=jnp.floataa)

																					SCREAMING_SNAKE_CASE_:				Dict							= convert_pytorch_state_dict_to_flax(pt_model.state_dict()     ,		lowerCAmelCase__)
																					SCREAMING_SNAKE_CASE_:				List[str]							= fx_state

																					with torch.no_grad():
																											SCREAMING_SNAKE_CASE_:				Tuple							= pt_model(**lowerCAmelCase__).to_tuple()

																					SCREAMING_SNAKE_CASE_:				Dict							= fx_model(**lowerCAmelCase__).to_tuple()
																					self.assertEqual(len(lowerCAmelCase__)     ,		len(lowerCAmelCase__)     ,		"Output lengths differ between Flax and PyTorch")
																					for fx_output, pt_output in zip(lowerCAmelCase__     ,		lowerCAmelCase__):
																											self.assert_almost_equals(fx_output[:, -1]     ,		pt_output[:, -1].numpy()     ,		4E-2)

																					with tempfile.TemporaryDirectory() as tmpdirname:
																											pt_model.save_pretrained(lowerCAmelCase__)
																											SCREAMING_SNAKE_CASE_:				List[str]							= model_class.from_pretrained(lowerCAmelCase__     ,		from_pt=lowerCAmelCase__)

																					SCREAMING_SNAKE_CASE_:				int							= fx_model_loaded(**lowerCAmelCase__).to_tuple()
																					self.assertEqual(
																					    len(lowerCAmelCase__)     ,		len(lowerCAmelCase__)     ,		"Output lengths differ between Flax and PyTorch")
																					for fx_output_loaded, pt_output in zip(lowerCAmelCase__     ,		lowerCAmelCase__):
																											self.assert_almost_equals(fx_output_loaded[:, -1]     ,		pt_output[:, -1].numpy()     ,		4E-2)



			@is_pt_flax_cross_test
			def  _SCREAMING_SNAKE_CASE    (				self						:  int):
									SCREAMING_SNAKE_CASE_	,					SCREAMING_SNAKE_CASE_:				Optional[int]							= self.model_tester.prepare_config_and_inputs_for_common()
									for model_class in self.all_model_classes:
															with self.subTest(model_class.__name__):
																					# prepare inputs
																					SCREAMING_SNAKE_CASE_:				str							= self._prepare_for_class(lowerCAmelCase__     ,		lowerCAmelCase__)
																					SCREAMING_SNAKE_CASE_:				Optional[Any]							= {k: torch.tensor(v.tolist()) for k, v in prepared_inputs_dict.items()}

																					# load corresponding PyTorch class
																					SCREAMING_SNAKE_CASE_:				Optional[int]							= model_class.__name__[4:]  # Skip the "Flax" at the beginning
																					SCREAMING_SNAKE_CASE_:				List[str]							= getattr(lowerCAmelCase__     ,		lowerCAmelCase__)

																					SCREAMING_SNAKE_CASE_:				Any							= pt_model_class(lowerCAmelCase__).eval()
																					SCREAMING_SNAKE_CASE_:				Optional[int]							= model_class(lowerCAmelCase__     ,		dtype=jnp.floataa)

																					SCREAMING_SNAKE_CASE_:				Optional[int]							= load_flax_weights_in_pytorch_model(lowerCAmelCase__     ,		fx_model.params)
																					SCREAMING_SNAKE_CASE_	,					SCREAMING_SNAKE_CASE_:				Optional[Any]							= pt_inputs["input_ids"].shape
																					SCREAMING_SNAKE_CASE_:				Any							= np.random.randint(0     ,		seq_length - 1     ,		size=(batch_size,))
																					for batch_idx, start_index in enumerate(lowerCAmelCase__):
																											SCREAMING_SNAKE_CASE_:				Any							= 0
																											SCREAMING_SNAKE_CASE_:				List[Any]							= 1
																											SCREAMING_SNAKE_CASE_:				Union[str, Any]							= 0
																											SCREAMING_SNAKE_CASE_:				Optional[Any]							= 1

																					# make sure weights are tied in PyTorch
																					pt_model.tie_weights()

																					with torch.no_grad():
																											SCREAMING_SNAKE_CASE_:				List[Any]							= pt_model(**lowerCAmelCase__).to_tuple()

																					SCREAMING_SNAKE_CASE_:				Union[str, Any]							= fx_model(**lowerCAmelCase__).to_tuple()
																					self.assertEqual(len(lowerCAmelCase__)     ,		len(lowerCAmelCase__)     ,		"Output lengths differ between Flax and PyTorch")
																					for fx_output, pt_output in zip(lowerCAmelCase__     ,		lowerCAmelCase__):
																											self.assert_almost_equals(fx_output[:, -1]     ,		pt_output[:, -1].numpy()     ,		4E-2)

																					with tempfile.TemporaryDirectory() as tmpdirname:
																											fx_model.save_pretrained(lowerCAmelCase__)
																											SCREAMING_SNAKE_CASE_:				List[Any]							= pt_model_class.from_pretrained(lowerCAmelCase__     ,		from_flax=lowerCAmelCase__)

																					with torch.no_grad():
																											SCREAMING_SNAKE_CASE_:				Union[str, Any]							= pt_model_loaded(**lowerCAmelCase__).to_tuple()

																					self.assertEqual(
																					    len(lowerCAmelCase__)     ,		len(lowerCAmelCase__)     ,		"Output lengths differ between Flax and PyTorch")
																					for fx_output, pt_output in zip(lowerCAmelCase__     ,		lowerCAmelCase__):
																											self.assert_almost_equals(fx_output[:, -1]     ,		pt_output[:, -1].numpy()     ,		4E-2)




			@slow
			def  _SCREAMING_SNAKE_CASE    (				self						:  Optional[int]):
									for model_class_name in self.all_model_classes:
															SCREAMING_SNAKE_CASE_:				List[str]							= model_class_name.from_pretrained("gpt2"     ,		from_pt=lowerCAmelCase__)
															SCREAMING_SNAKE_CASE_:				int							= model(np.ones((1, 1)))
															self.assertIsNotNone(lowerCAmelCase__)






