

'''simple docstring'''





import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
						import torch

if is_vision_available():
						from PIL import Image

						from transformers import ViltImageProcessor



class 						__lowercase       (				unittest.TestCase			):




			"""simple docstring"""







			def __init__(				self						:  Optional[int]     ,		lowerCAmelCase__						:  List[Any]     ,		lowerCAmelCase__						:  int=7     ,		lowerCAmelCase__						:  Union[str, Any]=3     ,		lowerCAmelCase__						:  Dict=18     ,		lowerCAmelCase__						:  int=30     ,		lowerCAmelCase__						:  Dict=400     ,		lowerCAmelCase__						:  Dict=True     ,		lowerCAmelCase__						:  Union[str, Any]=None     ,		lowerCAmelCase__						:  List[Any]=2     ,		lowerCAmelCase__						:  Any=True     ,		lowerCAmelCase__						:  str=[0.5, 0.5, 0.5]     ,		lowerCAmelCase__						:  str=[0.5, 0.5, 0.5]     ,		):
									SCREAMING_SNAKE_CASE_:				Optional[int]							= size if size is not None else {'shortest_edge': 30}
									SCREAMING_SNAKE_CASE_:				Dict							= parent
									SCREAMING_SNAKE_CASE_:				str							= batch_size
									SCREAMING_SNAKE_CASE_:				str							= num_channels
									SCREAMING_SNAKE_CASE_:				Optional[int]							= image_size
									SCREAMING_SNAKE_CASE_:				List[Any]							= min_resolution
									SCREAMING_SNAKE_CASE_:				Tuple							= max_resolution
									SCREAMING_SNAKE_CASE_:				Tuple							= do_resize
									SCREAMING_SNAKE_CASE_:				Any							= size
									SCREAMING_SNAKE_CASE_:				int							= size_divisor
									SCREAMING_SNAKE_CASE_:				str							= do_normalize
									SCREAMING_SNAKE_CASE_:				Optional[int]							= image_mean
									SCREAMING_SNAKE_CASE_:				List[str]							= image_std



			def  _SCREAMING_SNAKE_CASE    (				self						:  Tuple):
									return {
									    "image_mean": self.image_mean,
									    "image_std": self.image_std,
									    "do_normalize": self.do_normalize,
									    "do_resize": self.do_resize,
									    "size": self.size,
									    "size_divisor": self.size_divisor,
									}




			def  _SCREAMING_SNAKE_CASE    (				self						:  List[Any]     ,		lowerCAmelCase__						:  Optional[Any]     ,		lowerCAmelCase__						:  int=False):
									if not batched:
															SCREAMING_SNAKE_CASE_:				Optional[Any]							= self.size['shortest_edge']
															SCREAMING_SNAKE_CASE_:				int							= image_inputs[0]
															if isinstance(SCREAMING_SNAKE_CASE_     ,		Image.Image):
																					SCREAMING_SNAKE_CASE_:				List[Any]							= image.size
															else:
																					SCREAMING_SNAKE_CASE_:				List[Any]							= image.shape[1], image.shape[2]
															SCREAMING_SNAKE_CASE_:				Any							= size / min(SCREAMING_SNAKE_CASE_     ,		SCREAMING_SNAKE_CASE_)
															if h < w:
																					SCREAMING_SNAKE_CASE_:				Optional[int]							= size, scale * w
															else:
																					SCREAMING_SNAKE_CASE_:				List[str]							= scale * h, size

															SCREAMING_SNAKE_CASE_:				int							= int((1333 / 800) * size)
															if max(SCREAMING_SNAKE_CASE_     ,		SCREAMING_SNAKE_CASE_) > max_size:
																					SCREAMING_SNAKE_CASE_:				Any							= max_size / max(SCREAMING_SNAKE_CASE_     ,		SCREAMING_SNAKE_CASE_)
																					SCREAMING_SNAKE_CASE_:				List[Any]							= newh * scale
																					SCREAMING_SNAKE_CASE_:				Optional[int]							= neww * scale

															SCREAMING_SNAKE_CASE_:				List[Any]							= int(newh + 0.5), int(neww + 0.5)
															SCREAMING_SNAKE_CASE_:				int							= (
															    newh // self.size_divisor * self.size_divisor,
															    neww // self.size_divisor * self.size_divisor,
															)

									else:
															SCREAMING_SNAKE_CASE_:				Dict							= []
															for image in image_inputs:
																					SCREAMING_SNAKE_CASE_:				Union[str, Any]							= self.get_expected_values([image])
																					expected_values.append((expected_height, expected_width))
															SCREAMING_SNAKE_CASE_:				List[str]							= max(SCREAMING_SNAKE_CASE_     ,		key=lambda lowerCAmelCase__: item[0])[0]
															SCREAMING_SNAKE_CASE_:				List[str]							= max(SCREAMING_SNAKE_CASE_     ,		key=lambda lowerCAmelCase__: item[1])[1]

									return expected_height, expected_width







@require_torch
@require_vision
class 						__lowercase       (				a__	, unittest.TestCase			):




			"""simple docstring"""







			_UpperCAmelCase     :							List[Any]    							=  ViltImageProcessor if is_vision_available() else None



			def  _SCREAMING_SNAKE_CASE    (				self						:  int):
									SCREAMING_SNAKE_CASE_:				Dict							= ViltImageProcessingTester(self)



			@property
			def  _SCREAMING_SNAKE_CASE    (				self						:  Dict):
									return self.image_processor_tester.prepare_image_processor_dict()



			def  _SCREAMING_SNAKE_CASE    (				self						:  Optional[Any]):
									SCREAMING_SNAKE_CASE_:				List[str]							= self.image_processing_class(**self.image_processor_dict)
									self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_     ,		"image_mean"))
									self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_     ,		"image_std"))
									self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_     ,		"do_normalize"))
									self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_     ,		"do_resize"))
									self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_     ,		"size"))
									self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_     ,		"size_divisor"))



			def  _SCREAMING_SNAKE_CASE    (				self						:  Tuple):
									SCREAMING_SNAKE_CASE_:				Union[str, Any]							= self.image_processing_class.from_dict(self.image_processor_dict)
									self.assertEqual(image_processor.size     ,		{"shortest_edge": 30})

									SCREAMING_SNAKE_CASE_:				Union[str, Any]							= self.image_processing_class.from_dict(self.image_processor_dict     ,		size=42)
									self.assertEqual(image_processor.size     ,		{"shortest_edge": 42})



			def  _SCREAMING_SNAKE_CASE    (				self						:  Union[str, Any]):
									pass



			def  _SCREAMING_SNAKE_CASE    (				self						:  Dict):
									# Initialize image_processing
									SCREAMING_SNAKE_CASE_:				int							= self.image_processing_class(**self.image_processor_dict)
									# create random PIL images
									SCREAMING_SNAKE_CASE_:				Any							= prepare_image_inputs(self.image_processor_tester     ,		equal_resolution=SCREAMING_SNAKE_CASE_)
									for image in image_inputs:
															self.assertIsInstance(SCREAMING_SNAKE_CASE_     ,		Image.Image)

									# Test not batched input
									SCREAMING_SNAKE_CASE_:				int							= image_processing(image_inputs[0]     ,		return_tensors="pt").pixel_values

									SCREAMING_SNAKE_CASE_:				Dict							= self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_)
									self.assertEqual(
									    encoded_images.shape     ,		(1, self.image_processor_tester.num_channels, expected_height, expected_width)     ,		)

									# Test batched
									SCREAMING_SNAKE_CASE_:				str							= image_processing(SCREAMING_SNAKE_CASE_     ,		return_tensors="pt").pixel_values

									SCREAMING_SNAKE_CASE_:				Tuple							= self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_     ,		batched=SCREAMING_SNAKE_CASE_)
									self.assertEqual(
									    encoded_images.shape     ,		(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        expected_height,
									        expected_width,
									    )     ,		)



			def  _SCREAMING_SNAKE_CASE    (				self						:  List[Any]):
									# Initialize image_processing
									SCREAMING_SNAKE_CASE_:				Optional[int]							= self.image_processing_class(**self.image_processor_dict)
									# create random numpy tensors
									SCREAMING_SNAKE_CASE_:				Optional[Any]							= prepare_image_inputs(self.image_processor_tester     ,		equal_resolution=SCREAMING_SNAKE_CASE_     ,		numpify=SCREAMING_SNAKE_CASE_)
									for image in image_inputs:
															self.assertIsInstance(SCREAMING_SNAKE_CASE_     ,		np.ndarray)

									# Test not batched input
									SCREAMING_SNAKE_CASE_:				Optional[Any]							= image_processing(image_inputs[0]     ,		return_tensors="pt").pixel_values

									SCREAMING_SNAKE_CASE_:				str							= self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_)
									self.assertEqual(
									    encoded_images.shape     ,		(1, self.image_processor_tester.num_channels, expected_height, expected_width)     ,		)

									# Test batched
									SCREAMING_SNAKE_CASE_:				Dict							= image_processing(SCREAMING_SNAKE_CASE_     ,		return_tensors="pt").pixel_values

									SCREAMING_SNAKE_CASE_:				Optional[Any]							= self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_     ,		batched=SCREAMING_SNAKE_CASE_)
									self.assertEqual(
									    encoded_images.shape     ,		(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        expected_height,
									        expected_width,
									    )     ,		)




			def  _SCREAMING_SNAKE_CASE    (				self						:  Any):
									# Initialize image_processing
									SCREAMING_SNAKE_CASE_:				Optional[int]							= self.image_processing_class(**self.image_processor_dict)
									# create random PyTorch tensors
									SCREAMING_SNAKE_CASE_:				Dict							= prepare_image_inputs(self.image_processor_tester     ,		equal_resolution=SCREAMING_SNAKE_CASE_     ,		torchify=SCREAMING_SNAKE_CASE_)
									for image in image_inputs:
															self.assertIsInstance(SCREAMING_SNAKE_CASE_     ,		torch.Tensor)

									# Test not batched input
									SCREAMING_SNAKE_CASE_:				List[str]							= image_processing(image_inputs[0]     ,		return_tensors="pt").pixel_values

									SCREAMING_SNAKE_CASE_:				Union[str, Any]							= self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_)
									self.assertEqual(
									    encoded_images.shape     ,		(1, self.image_processor_tester.num_channels, expected_height, expected_width)     ,		)

									# Test batched
									SCREAMING_SNAKE_CASE_:				List[Any]							= image_processing(SCREAMING_SNAKE_CASE_     ,		return_tensors="pt").pixel_values

									SCREAMING_SNAKE_CASE_:				Dict							= self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_     ,		batched=SCREAMING_SNAKE_CASE_)
									self.assertEqual(
									    encoded_images.shape     ,		(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        expected_height,
									        expected_width,
									    )     ,		)









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__)






