





import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device


if is_torch_available():
				import torch

				from transformers import XLMProphetNetForConditionalGeneration, XLMProphetNetTokenizer
@require_torch
class 							snake_case_		(      unittest.TestCase):







		@slow
		def       __lowercase (		self )					-> Union[str, Any]:
				lowerCamelCase :			Union[str, Any]    		=XLMProphetNetForConditionalGeneration.from_pretrained('''microsoft/xprophetnet-large-wiki100-cased''' )
				model.to(__lowercase )

				# encoder-decoder outputs
				lowerCamelCase :			str    		=torch.tensor([[1_7, 9_6_2_0_8, 1_0_3_4_7_1, 2]] ).to(__lowercase )
				lowerCamelCase :			Any    		=torch.tensor(
				    [[2, 2_5_0, 9_9_5_3, 3_4, 6_9_4_8_9, 1_6_2_0, 3_2, 1_1_8_4_2_4, 6_2_4, 2_1_0, 1_0_5, 2_9_1_3, 1_0_3_2, 3_5_1]] ).to(__lowercase )
				lowerCamelCase :			Tuple    		=model(
				    input_ids=__lowercase      ,  attention_mask=__lowercase      ,  encoder_outputs=__lowercase      ,  decoder_input_ids=__lowercase )
				lowerCamelCase :			Optional[Any]    		=output[0]
				lowerCamelCase :			Tuple    		=torch.Size((1, 1_4, 2_5_0_0_1_2) )
				self.assertEqual(output_predited_logis.shape      ,  __lowercase )
				lowerCamelCase :			str    		=torch.tensor(
				    [[[-6.3_9_8_6, -8.2_3_9_1, 1_2.5_1_8_9], [-6.3_2_8_9, -8.0_8_6_4, 1_2.6_2_1_1], [-6.2_4_1_8, -8.0_4_4_5, 1_2.7_9_6_8]]] ).to(__lowercase )
				self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3]      ,  __lowercase      ,  atol=1e-4 ) )

				# encoder outputs
				lowerCamelCase :			Union[str, Any]    		=model.prophetnet.encoder(__lowercase )[0]
				lowerCamelCase :			List[str]    		=torch.tensor(
				    [[[-1.4_2_6_0, -0.7_6_2_8, 0.8_4_5_3], [-1.4_7_1_9, -0.1_3_9_1, 0.7_8_0_7], [-1.7_6_7_8, 0.0_1_1_4, 0.4_6_4_6]]] ).to(__lowercase )
				lowerCamelCase :			List[str]    		=torch.Size((1, 4, 1_0_2_4) )
				self.assertEqual(encoder_outputs.shape      ,  __lowercase )
				self.assertTrue(torch.allclose(encoder_outputs[:, :3, :3]      ,  __lowercase      ,  atol=1e-4 ) )

				# decoder outputs
				lowerCamelCase :			Optional[Any]    		=model.prophetnet.decoder(
				    __lowercase      ,  encoder_hidden_states=__lowercase      ,  )
				lowerCamelCase :			Optional[Any]    		=decoder_outputs[1].view(1      ,  model.config.ngram      ,  1_4      ,  -1 )
				lowerCamelCase :			Dict    		=model.lm_head(__lowercase )
				lowerCamelCase :			List[str]    		=predicting_streams_logits[:, 0]
				self.assertTrue(torch.allclose(next_first_stream_logits[:, :3, :3]      ,  __lowercase      ,  atol=1e-4 ) )







		@slow
		def       __lowercase (		self )					-> Tuple:
				lowerCamelCase :			Optional[Any]    		=XLMProphetNetForConditionalGeneration.from_pretrained(
				    '''microsoft/xprophetnet-large-wiki100-cased-xglue-ntg''' )
				model.to(__lowercase )

				lowerCamelCase :			Any    		=torch.tensor([[1_7, 9_6_2_0_8, 1_0_3_4_7_1, 2]] ).to(__lowercase )
				lowerCamelCase :			int    		=torch.tensor(
				    [[2, 2_5_0, 9_9_5_3, 3_4, 6_9_4_8_9, 1_6_2_0, 3_2, 1_1_8_4_2_4, 6_2_4, 2_1_0, 1_0_5, 2_9_1_3, 1_0_3_2, 3_5_1]] ).to(__lowercase )
				lowerCamelCase :			Any    		=model(
				    input_ids=__lowercase      ,  attention_mask=__lowercase      ,  encoder_outputs=__lowercase      ,  decoder_input_ids=__lowercase )
				lowerCamelCase :			Optional[Any]    		=output[0]
				lowerCamelCase :			List[Any]    		=torch.Size((1, 1_4, 2_5_0_0_1_2) )
				self.assertEqual(output_predited_logis.shape      ,  __lowercase )
				# compare the actual values for a slice.
				lowerCamelCase :			str    		=torch.tensor(
				    [[[-9.2_2_5_3, -9.7_1_7_3, -6.3_5_2_9], [-7.6_7_0_1, -9.0_1_4_5, -1.9_3_8_2], [-8.0_1_9_5, -7.0_0_0_4, -0.1_5_2_3]]] ).to(__lowercase )

				self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3]      ,  __lowercase      ,  atol=1e-4 ) )







		@slow
		def       __lowercase (		self )					-> Union[str, Any]:
				lowerCamelCase :			Tuple    		=XLMProphetNetForConditionalGeneration.from_pretrained(
				    '''microsoft/xprophetnet-large-wiki100-cased-xglue-ntg''' )
				model.to(__lowercase )
				lowerCamelCase :			List[Any]    		=5_1_2

				lowerCamelCase :			List[Any]    		=XLMProphetNetTokenizer.from_pretrained('''microsoft/xprophetnet-large-wiki100-cased-xglue-ntg''' )

				lowerCamelCase :			Any    		=(
				    '''Microsoft Corporation intends to officially end free support for the Windows 7 operating system after'''
				    ''' January 14, 2020, according to the official portal of the organization. From that day, users of this'''
				    ''' system will not be able to receive security updates, which could make their computers vulnerable to'''
				    ''' cyber attacks.'''
				)
				lowerCamelCase :			str    		=(
				    '''орпорация Microsoft намерена официально прекратить бесплатную поддержку операционной системы Windows 7'''
				    ''' после 14 января 2020 года, сообщается на официальном портале организации . С указанного дня пользователи'''
				    ''' этой системы не смогут получать обновления безопасности, из-за чего их компьютеры могут стать уязвимыми'''
				    ''' к кибератакам.'''
				)
				lowerCamelCase :			Optional[int]    		=(
				    '''根据该组织的官方门户网站，微软公司打算在2020年1月14日之后正式终止对Windows 7操作系统的免费支持。从那时起，该系统的用户将无法接收安全更新，这可能会使他们的计算机容易受到网络攻击。'''
				)

				lowerCamelCase :			List[Any]    		=tokenizer(
				    [EN_SENTENCE, RU_SENTENCE, ZH_SENTENCE]      ,  padding=__lowercase      ,  max_length=2_5_5      ,  return_tensors='''pt''' ).input_ids
				lowerCamelCase :			Union[str, Any]    		=input_ids.to(__lowercase )

				lowerCamelCase :			Optional[int]    		=model.generate(
				    __lowercase      ,  num_beams=1_0      ,  length_penalty=1.0      ,  no_repeat_ngram_size=3      ,  early_stopping=__lowercase )
				lowerCamelCase :			List[Any]    		=[tokenizer.decode(__lowercase      ,  skip_special_tokens=__lowercase ) for g in summary_ids]
				lowerCamelCase :			Tuple    		='''Microsoft to end Windows 7 free support after January 14, 2020'''
				lowerCamelCase :			List[Any]    		='''Microsoft намерена прекратить бесплатную поддержку Windows 7 после 14 января 2020 года'''
				lowerCamelCase :			List[Any]    		='''微软打算终止对Windows 7操作系统的免费支持'''
				self.assertListEqual(
				    [EXPECTED_TITLE_EN, EXPECTED_TITLE_RU, EXPECTED_TITLE_ZH]      ,  __lowercase      ,  )

				lowerCamelCase :			str    		=model.generate(
				    __lowercase      ,  num_beams=1      ,  length_penalty=1.0      ,  no_repeat_ngram_size=3      ,  early_stopping=__lowercase )
				lowerCamelCase :			Union[str, Any]    		=[
				    tokenizer.convert_ids_to_tokens(__lowercase      ,  skip_special_tokens=__lowercase ) for g in summary_ids_beama
				]
				lowerCamelCase :			Any    		='''▁Microsoft ▁to ▁end ▁free ▁support ▁for ▁Windows ▁7'''.split(''' ''' )
				lowerCamelCase :			str    		='''▁Microsoft ▁намерен а ▁прекрати ть ▁бес плат ную ▁поддержку ▁Windows ▁7 ▁после ▁14 ▁января ▁2020 ▁года'''.split(
				    ''' ''' )
				lowerCamelCase :			List[str]    		='''微软 公司 打算 终止 对 Windows ▁7 操作 系统的 免费 支持'''.split(''' ''' )
				self.assertListEqual(
				    [EXPECTED_TITLE_EN_BEAM1_TOK, EXPECTED_TITLE_RU_BEAM1_TOK, EXPECTED_TITLE_ZH_BEAM1_TOK]      ,  __lowercase      ,  )








import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTNeoConfig, 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, 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.gpt_neo.modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel

if is_torch_available():
				import torch
class 							snake_case_		:







		def __init__(		self      ,  __lowercase      ,  __lowercase=1_4      ,  __lowercase=7      ,  __lowercase=True      ,  __lowercase=True      ,  __lowercase=False      ,  __lowercase=True      ,  __lowercase=9_9      ,  __lowercase=3_2      ,  __lowercase=4      ,  __lowercase=4      ,  __lowercase=[[["global", "local"], 2]]      ,  __lowercase=3_7      ,  __lowercase="gelu"      ,  __lowercase=0.1      ,  __lowercase=0.1      ,  __lowercase=5_1_2      ,  __lowercase=7      ,  __lowercase=0.0_2      ,  )					-> Tuple:
				lowerCamelCase :			Optional[Any]    		=parent
				lowerCamelCase :			Optional[int]    		=batch_size
				lowerCamelCase :			Any    		=seq_length
				lowerCamelCase :			Optional[Any]    		=is_training
				lowerCamelCase :			Any    		=use_input_mask
				lowerCamelCase :			int    		=use_token_type_ids
				lowerCamelCase :			int    		=use_labels
				lowerCamelCase :			str    		=vocab_size
				lowerCamelCase :			Optional[int]    		=hidden_size
				lowerCamelCase :			int    		=num_hidden_layers
				lowerCamelCase :			List[Any]    		=num_attention_heads
				lowerCamelCase :			Any    		=attention_types
				lowerCamelCase :			List[str]    		=intermediate_size
				lowerCamelCase :			Dict    		=hidden_act
				lowerCamelCase :			Union[str, Any]    		=hidden_dropout_prob
				lowerCamelCase :			Optional[int]    		=attention_probs_dropout_prob
				lowerCamelCase :			Any    		=max_position_embeddings
				lowerCamelCase :			int    		=window_size
				lowerCamelCase :			List[Any]    		=initializer_range
				lowerCamelCase :			Any    		=None
				lowerCamelCase :			List[Any]    		=vocab_size - 1
				lowerCamelCase :			str    		=vocab_size - 1
				lowerCamelCase :			int    		=vocab_size - 1







		def       __lowercase (		self )					-> Dict:
				lowerCamelCase :			List[Any]    		=ids_tensor([self.batch_size, self.seq_length]      ,  self.vocab_size )

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

				lowerCamelCase :			List[Any]    		=GPTNeoConfig(
				    vocab_size=self.vocab_size      ,  hidden_size=self.hidden_size      ,  num_layers=self.num_hidden_layers      ,  num_heads=self.num_attention_heads      ,  max_position_embeddings=self.max_position_embeddings      ,  use_cache=__lowercase      ,  bos_token_id=self.bos_token_id      ,  eos_token_id=self.eos_token_id      ,  pad_token_id=self.pad_token_id      ,  window_size=self.window_size      ,  attention_types=self.attention_types      ,  )

				return (config, input_ids, input_mask)







		def       __lowercase (		self )					-> Any:
				lowerCamelCase :			List[Any]    		=self.prepare_config_and_inputs()
				lowerCamelCase		, lowerCamelCase		, lowerCamelCase :			Optional[Any]    		=config_and_inputs
				lowerCamelCase :			List[str]    		={'''input_ids''': input_ids, '''attention_mask''': attention_mask}
				return config, inputs_dict







		def       __lowercase (		self      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase )					-> List[Any]:
				lowerCamelCase :			Tuple    		=2_0
				lowerCamelCase :			Union[str, Any]    		=model_class_name(__lowercase )

				lowerCamelCase :			List[Any]    		=model.init_cache(input_ids.shape[0]      ,  __lowercase )
				lowerCamelCase :			str    		=jnp.ones((input_ids.shape[0], max_decoder_length)      ,  dtype='''i4''' )

				lowerCamelCase :			Optional[int]    		=jnp.broadcast_to(
				    jnp.arange(input_ids.shape[-1] - 1 )[None, :]      ,  (input_ids.shape[0], input_ids.shape[-1] - 1) )
				lowerCamelCase :			str    		=model(
				    input_ids[:, :-1]      ,  attention_mask=__lowercase      ,  past_key_values=__lowercase      ,  position_ids=__lowercase      ,  )

				lowerCamelCase :			List[str]    		=jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]      ,  dtype='''i4''' )
				lowerCamelCase :			List[Any]    		=model(
				    input_ids[:, -1:]      ,  attention_mask=__lowercase      ,  past_key_values=outputs_cache.past_key_values      ,  position_ids=__lowercase      ,  )

				lowerCamelCase :			Any    		=model(__lowercase )

				lowerCamelCase :			Dict    		=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      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase )					-> Dict:
				lowerCamelCase :			str    		=2_0
				lowerCamelCase :			Optional[int]    		=model_class_name(__lowercase )

				lowerCamelCase :			int    		=jnp.concatenate(
				    [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1]) )]      ,  axis=-1      ,  )

				lowerCamelCase :			Union[str, Any]    		=model.init_cache(input_ids.shape[0]      ,  __lowercase )
				lowerCamelCase :			List[Any]    		=jnp.broadcast_to(
				    jnp.arange(input_ids.shape[-1] - 1 )[None, :]      ,  (input_ids.shape[0], input_ids.shape[-1] - 1) )

				lowerCamelCase :			Optional[Any]    		=model(
				    input_ids[:, :-1]      ,  attention_mask=__lowercase      ,  past_key_values=__lowercase      ,  position_ids=__lowercase      ,  )
				lowerCamelCase :			List[Any]    		=jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]      ,  dtype='''i4''' )
				lowerCamelCase :			Union[str, Any]    		=model(
				    input_ids[:, -1:]      ,  past_key_values=outputs_cache.past_key_values      ,  attention_mask=__lowercase      ,  position_ids=__lowercase      ,  )

				lowerCamelCase :			Union[str, Any]    		=model(__lowercase      ,  attention_mask=__lowercase )

				lowerCamelCase :			Dict    		=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 							snake_case_		(      _A    ,       _A    ,       unittest.TestCase):
		lowerCamelCase     :List[Any]							=  (FlaxGPTNeoModel, FlaxGPTNeoForCausalLM) if is_flax_available() else ()
		lowerCamelCase     :Union[str, Any]							=  (FlaxGPTNeoForCausalLM,) if is_flax_available() else ()







		def       __lowercase (		self )					-> Union[str, Any]:
				lowerCamelCase :			str    		=FlaxGPTNeoModelTester(self )







		def       __lowercase (		self )					-> Dict:
				for model_class_name in self.all_model_classes:
						lowerCamelCase		, lowerCamelCase		, lowerCamelCase :			Dict    		=self.model_tester.prepare_config_and_inputs()
						self.model_tester.check_use_cache_forward(__lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase )







		def       __lowercase (		self )					-> Tuple:
				for model_class_name in self.all_model_classes:
						lowerCamelCase		, lowerCamelCase		, lowerCamelCase :			Optional[int]    		=self.model_tester.prepare_config_and_inputs()
						self.model_tester.check_use_cache_forward_with_attn_mask(
						    __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase )







		@slow
		def       __lowercase (		self )					-> Optional[int]:
				lowerCamelCase :			Tuple    		=GPTaTokenizer.from_pretrained('''gpt2'''      ,  pad_token='''<|endoftext|>'''      ,  padding_side='''left''' )
				lowerCamelCase :			Any    		=tokenizer(['''Hello this is a long string''', '''Hey''']      ,  return_tensors='''np'''      ,  padding=__lowercase      ,  truncation=__lowercase )

				lowerCamelCase :			List[Any]    		=FlaxGPTNeoForCausalLM.from_pretrained('''EleutherAI/gpt-neo-125M''' )
				lowerCamelCase :			Union[str, Any]    		=False
				lowerCamelCase :			Optional[int]    		=model.config.eos_token_id

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

				lowerCamelCase :			Optional[int]    		=jit_generate(
				    inputs['''input_ids''']      ,  attention_mask=inputs['''attention_mask''']      ,  pad_token_id=tokenizer.pad_token_id ).sequences

				lowerCamelCase :			Any    		=tokenizer.batch_decode(__lowercase      ,  skip_special_tokens=__lowercase )

				lowerCamelCase :			str    		=[
				    '''Hello this is a long string of text.\n\nI\'m trying to get the text of the''',
				    '''Hey, I\'m a little late to the party. I\'m going to''',
				]

				self.assertListEqual(__lowercase      ,  __lowercase )







		@is_pt_flax_cross_test
		def       __lowercase (		self )					-> int:
				lowerCamelCase		, lowerCamelCase :			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
								lowerCamelCase :			Optional[Any]    		=self._prepare_for_class(__lowercase      ,  __lowercase )
								lowerCamelCase :			Optional[Any]    		={k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}

								# load corresponding PyTorch class
								lowerCamelCase :			Any    		=model_class.__name__[4:]  # Skip the "Flax" at the beginning
								lowerCamelCase :			Optional[int]    		=getattr(__lowercase      ,  __lowercase )

								lowerCamelCase		, lowerCamelCase :			Union[str, Any]    		=pt_inputs['''input_ids'''].shape
								lowerCamelCase :			Dict    		=np.random.randint(0      ,  seq_length - 1      ,  size=(batch_size,) )
								for batch_idx, start_index in enumerate(__lowercase ):
										lowerCamelCase :			Tuple    		=0
										lowerCamelCase :			Optional[Any]    		=1
										lowerCamelCase :			Tuple    		=0
										lowerCamelCase :			Union[str, Any]    		=1
								lowerCamelCase :			Optional[int]    		=pt_model_class(__lowercase ).eval()
								lowerCamelCase :			Any    		=model_class(__lowercase      ,  dtype=jnp.floataa )

								lowerCamelCase :			Union[str, Any]    		=convert_pytorch_state_dict_to_flax(pt_model.state_dict()      ,  __lowercase )
								lowerCamelCase :			Optional[int]    		=fx_state

								with torch.no_grad():
										lowerCamelCase :			List[Any]    		=pt_model(**__lowercase ).to_tuple()

								lowerCamelCase :			List[Any]    		=fx_model(**__lowercase ).to_tuple()
								self.assertEqual(len(__lowercase )      ,  len(__lowercase )      ,  '''Output lengths differ between Flax and PyTorch''' )
								for fx_output, pt_output in zip(__lowercase      ,  __lowercase ):
										self.assert_almost_equals(fx_output[:, -1]      ,  pt_output[:, -1].numpy()      ,  4e-2 )

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

								lowerCamelCase :			List[str]    		=fx_model_loaded(**__lowercase ).to_tuple()
								self.assertEqual(
								    len(__lowercase )      ,  len(__lowercase )      ,  '''Output lengths differ between Flax and PyTorch''' )
								for fx_output_loaded, pt_output in zip(__lowercase      ,  __lowercase ):
										self.assert_almost_equals(fx_output_loaded[:, -1]      ,  pt_output[:, -1].numpy()      ,  4e-2 )







		@is_pt_flax_cross_test
		def       __lowercase (		self )					-> str:
				lowerCamelCase		, lowerCamelCase :			Dict    		=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
								lowerCamelCase :			Tuple    		=self._prepare_for_class(__lowercase      ,  __lowercase )
								lowerCamelCase :			Any    		={k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}

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

								lowerCamelCase :			List[str]    		=pt_model_class(__lowercase ).eval()
								lowerCamelCase :			Union[str, Any]    		=model_class(__lowercase      ,  dtype=jnp.floataa )

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

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

								with torch.no_grad():
										lowerCamelCase :			Tuple    		=pt_model(**__lowercase ).to_tuple()

								lowerCamelCase :			Optional[Any]    		=fx_model(**__lowercase ).to_tuple()
								self.assertEqual(len(__lowercase )      ,  len(__lowercase )      ,  '''Output lengths differ between Flax and PyTorch''' )
								for fx_output, pt_output in zip(__lowercase      ,  __lowercase ):
										self.assert_almost_equals(fx_output[:, -1]      ,  pt_output[:, -1].numpy()      ,  4e-2 )

								with tempfile.TemporaryDirectory() as tmpdirname:
										fx_model.save_pretrained(__lowercase )
										lowerCamelCase :			Union[str, Any]    		=pt_model_class.from_pretrained(__lowercase      ,  from_flax=__lowercase )

								with torch.no_grad():
										lowerCamelCase :			str    		=pt_model_loaded(**__lowercase ).to_tuple()

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







		@slow
		def       __lowercase (		self )					-> Any:
				for model_class_name in self.all_model_classes:
						lowerCamelCase :			Optional[Any]    		=model_class_name.from_pretrained('''EleutherAI/gpt-neo-125M''' )
						lowerCamelCase :			Union[str, Any]    		=model(np.ones((1, 1) ) )
						self.assertIsNotNone(__lowercase )

