"""simple docstring"""




import collections
import tempfile
import unittest

import numpy as np

from transformers.testing_utils import (
    is_pt_flax_cross_test,
    require_flax,
    require_torch,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import is_flax_available, is_torch_available, is_vision_available

from ...test_modeling_flax_common import floats_tensor, ids_tensor, random_attention_mask
from ..bert.test_modeling_flax_bert import FlaxBertModelTester
from ..clip.test_modeling_flax_clip import FlaxCLIPVisionModelTester
from ..vit.test_modeling_flax_vit import FlaxViTModelTester


if is_flax_available():
			from transformers import (
			    FlaxBertModel,
			    FlaxCLIPVisionModel,
			    FlaxVisionTextDualEncoderModel,
			    FlaxViTModel,
			    VisionTextDualEncoderConfig,
			    VisionTextDualEncoderProcessor,
			)
			from transformers.modeling_flax_pytorch_utils import (
			    convert_pytorch_state_dict_to_flax,
			    load_flax_weights_in_pytorch_model,
			)


if is_torch_available():
			import torch

			from transformers import VisionTextDualEncoderModel

if is_vision_available():
			from PIL import Image







def 							_UpperCAmelCase		(       lowerCamelCase__     ):





		"""simple docstring"""




		if isinstance(lowerCamelCase__			,  collections.abc.Iterable     ):
				return x
		return (x, x)

@require_flax
class       a_      :




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




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




					def 						_SCREAMING_SNAKE_CASE						(	self						:     int     ):
							pass




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[Any]  ,						snake_case__						:     np.ndarray  ,						snake_case__						:     np.ndarray  ,						snake_case__						:     float     ):
							lowerCAmelCase__        =   np.abs((a - b)     ).max()
							self.assertLessEqual(snake_case__  ,						snake_case__  ,						F"""Difference between torch and flax is {diff} (>= {tol})."""     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[str]  ,						snake_case__						:     str  ,						snake_case__						:     Dict  ,						snake_case__						:     str  ,						snake_case__						:     Optional[Any]  ,						snake_case__						:     str=None  ,						**snake_case__						:     Optional[Any]     ):
							lowerCAmelCase__        =   VisionTextDualEncoderConfig.from_vision_text_configs(snake_case__  ,						snake_case__     )

							lowerCAmelCase__        =   FlaxVisionTextDualEncoderModel(snake_case__     )

							lowerCAmelCase__        =   model(input_ids=snake_case__  ,						pixel_values=snake_case__  ,						attention_mask=snake_case__     )

							self.assertEqual(output["""text_embeds"""].shape  ,						(input_ids.shape[0], config.projection_dim)     )
							self.assertEqual(output["""image_embeds"""].shape  ,						(pixel_values.shape[0], config.projection_dim)     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     str  ,						snake_case__						:     Tuple  ,						snake_case__						:     Optional[int]  ,						snake_case__						:     Any  ,						snake_case__						:     Dict  ,						snake_case__						:     Optional[int]=None  ,						**snake_case__						:     Tuple     ):
							lowerCAmelCase__   ,					lowerCAmelCase__        =   self.get_vision_text_model(snake_case__  ,						snake_case__     )
							lowerCAmelCase__        =   {"""vision_model""": vision_model, """text_model""": text_model}
							lowerCAmelCase__        =   FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**snake_case__     )

							lowerCAmelCase__        =   model(input_ids=snake_case__  ,						pixel_values=snake_case__  ,						attention_mask=snake_case__     )

							self.assertEqual(output["""text_embeds"""].shape  ,						(input_ids.shape[0], model.config.projection_dim)     )
							self.assertEqual(output["""image_embeds"""].shape  ,						(pixel_values.shape[0], model.config.projection_dim)     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Dict  ,						snake_case__						:     List[Any]  ,						snake_case__						:     List[Any]  ,						snake_case__						:     int  ,						snake_case__						:     str  ,						snake_case__						:     int=None  ,						**snake_case__						:     Optional[Any]     ):
							lowerCAmelCase__   ,					lowerCAmelCase__        =   self.get_vision_text_model(snake_case__  ,						snake_case__     )
							lowerCAmelCase__        =   {"""vision_model""": vision_model, """text_model""": text_model}
							lowerCAmelCase__        =   FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**snake_case__     )

							lowerCAmelCase__        =   model(input_ids=snake_case__  ,						pixel_values=snake_case__  ,						attention_mask=snake_case__     )
							lowerCAmelCase__        =   output[0]

							with tempfile.TemporaryDirectory() as tmpdirname:
									model.save_pretrained(snake_case__     )
									lowerCAmelCase__        =   FlaxVisionTextDualEncoderModel.from_pretrained(snake_case__     )

									lowerCAmelCase__        =   model(input_ids=snake_case__  ,						pixel_values=snake_case__  ,						attention_mask=snake_case__     )
									lowerCAmelCase__        =   after_output[0]
									lowerCAmelCase__        =   np.amax(np.abs(out_a - out_a     )     )
									self.assertLessEqual(snake_case__  ,						1E-3     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Any  ,						snake_case__						:     List[Any]  ,						snake_case__						:     Optional[int]  ,						snake_case__						:     Optional[Any]  ,						snake_case__						:     int  ,						snake_case__						:     List[str]=None  ,						**snake_case__						:     List[str]     ):
							lowerCAmelCase__   ,					lowerCAmelCase__        =   self.get_vision_text_model(snake_case__  ,						snake_case__     )
							lowerCAmelCase__        =   {"""vision_model""": vision_model, """text_model""": text_model}
							lowerCAmelCase__        =   FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**snake_case__     )

							lowerCAmelCase__        =   model(
							    input_ids=snake_case__  ,						pixel_values=snake_case__  ,						attention_mask=snake_case__  ,						output_attentions=snake_case__     )

							lowerCAmelCase__        =   output.vision_model_output.attentions
							self.assertEqual(len(snake_case__     )  ,						vision_config.num_hidden_layers     )

							# in ViT, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
							lowerCAmelCase__        =   to_atuple(vision_model.config.image_size     )
							lowerCAmelCase__        =   to_atuple(vision_model.config.patch_size     )
							lowerCAmelCase__        =   (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
							lowerCAmelCase__        =   num_patches + 1
							self.assertEqual(vision_attentions[0].shape[-3:]  ,						(vision_config.num_attention_heads, seq_len, seq_len)     )

							lowerCAmelCase__        =   output.text_model_output.attentions
							self.assertEqual(len(snake_case__     )  ,						text_config.num_hidden_layers     )

							self.assertEqual(
							    text_attentions[0].shape[-3:]  ,						(text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1])  ,						)




					def 						_SCREAMING_SNAKE_CASE						(	self						:     int  ,						snake_case__						:     List[str]  ,						snake_case__						:     Any  ,						snake_case__						:     int     ):
							pt_model.to(snake_case__     )
							pt_model.eval()

							# prepare inputs
							lowerCAmelCase__        =   inputs_dict
							lowerCAmelCase__        =   {k: torch.tensor(v.tolist()     ) for k, v in flax_inputs.items()}

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

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

							# PT -> Flax
							with tempfile.TemporaryDirectory() as tmpdirname:
									pt_model.save_pretrained(snake_case__     )
									lowerCAmelCase__        =   FlaxVisionTextDualEncoderModel.from_pretrained(snake_case__  ,						from_pt=snake_case__     )

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

							# Flax -> PT
							with tempfile.TemporaryDirectory() as tmpdirname:
									fx_model.save_pretrained(snake_case__     )
									lowerCAmelCase__        =   VisionTextDualEncoderModel.from_pretrained(snake_case__  ,						from_flax=snake_case__     )

							pt_model_loaded.to(snake_case__     )
							pt_model_loaded.eval()

							with torch.no_grad():
									lowerCAmelCase__        =   pt_model_loaded(**snake_case__     ).to_tuple()

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




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[Any]  ,						snake_case__						:     List[str]  ,						snake_case__						:     List[Any]  ,						snake_case__						:     Optional[Any]     ):
							lowerCAmelCase__        =   VisionTextDualEncoderConfig.from_vision_text_configs(snake_case__  ,						snake_case__     )

							lowerCAmelCase__        =   VisionTextDualEncoderModel(snake_case__     )
							lowerCAmelCase__        =   FlaxVisionTextDualEncoderModel(snake_case__     )

							lowerCAmelCase__        =   convert_pytorch_state_dict_to_flax(pt_model.state_dict()  ,						snake_case__     )
							lowerCAmelCase__        =   fx_state

							self.check_pt_flax_equivalence(snake_case__  ,						snake_case__  ,						snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     str  ,						snake_case__						:     Any  ,						snake_case__						:     Any  ,						snake_case__						:     Tuple     ):
							lowerCAmelCase__        =   VisionTextDualEncoderConfig.from_vision_text_configs(snake_case__  ,						snake_case__     )

							lowerCAmelCase__        =   VisionTextDualEncoderModel(snake_case__     )
							lowerCAmelCase__        =   FlaxVisionTextDualEncoderModel(snake_case__     )

							lowerCAmelCase__        =   load_flax_weights_in_pytorch_model(snake_case__  ,						fx_model.params     )

							self.check_pt_flax_equivalence(snake_case__  ,						snake_case__  ,						snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Dict     ):
							lowerCAmelCase__        =   self.prepare_config_and_inputs()
							self.check_model_from_pretrained_configs(**snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[Any]     ):
							lowerCAmelCase__        =   self.prepare_config_and_inputs()
							self.check_vision_text_dual_encoder_from_pretrained(**snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[Any]     ):
							lowerCAmelCase__        =   self.prepare_config_and_inputs()
							self.check_save_load(**snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[str]     ):
							lowerCAmelCase__        =   self.prepare_config_and_inputs()
							self.check_vision_text_output_attention(**snake_case__     )




					@is_pt_flax_cross_test
					def 						_SCREAMING_SNAKE_CASE						(	self						:     str     ):
							lowerCAmelCase__        =   self.prepare_config_and_inputs()
							lowerCAmelCase__        =   config_inputs_dict.pop("""vision_config"""     )
							lowerCAmelCase__        =   config_inputs_dict.pop("""text_config"""     )

							lowerCAmelCase__        =   config_inputs_dict

							self.check_equivalence_pt_to_flax(snake_case__  ,						snake_case__  ,						snake_case__     )
							self.check_equivalence_flax_to_pt(snake_case__  ,						snake_case__  ,						snake_case__     )




					@slow
					def 						_SCREAMING_SNAKE_CASE						(	self						:     Union[str, Any]     ):
							lowerCAmelCase__   ,					lowerCAmelCase__        =   self.get_pretrained_model_and_inputs()

							lowerCAmelCase__        =   model_a(**snake_case__     )
							lowerCAmelCase__        =   outputs[0]

							with tempfile.TemporaryDirectory() as tmp_dirname:
									model_a.save_pretrained(snake_case__     )
									lowerCAmelCase__        =   FlaxVisionTextDualEncoderModel.from_pretrained(snake_case__     )

									lowerCAmelCase__        =   model_a(**snake_case__     )
									lowerCAmelCase__        =   after_outputs[0]
									lowerCAmelCase__        =   np.amax(np.abs(out_a - out_a     )     )
									self.assertLessEqual(snake_case__  ,						1E-5     )

@require_flax
class       a_      (     __UpperCamelCase ,		unittest.TestCase			):




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Union[str, Any]     ):
							lowerCAmelCase__        =   FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
							    """hf-internal-testing/tiny-random-vit"""  ,						"""hf-internal-testing/tiny-bert"""  ,						vision_from_pt=snake_case__  ,						text_from_pt=snake_case__  ,						)
							lowerCAmelCase__        =   13
							lowerCAmelCase__        =   floats_tensor(
							    [
							        batch_size,
							        model.config.vision_config.num_channels,
							        model.config.vision_config.image_size,
							        model.config.vision_config.image_size,
							    ]     )
							lowerCAmelCase__        =   ids_tensor([batch_size, 4]  ,						model.config.text_config.vocab_size     )
							lowerCAmelCase__        =   random_attention_mask([batch_size, 4]     )
							lowerCAmelCase__        =   {"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}

							return model, inputs




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[str]  ,						snake_case__						:     str  ,						snake_case__						:     Optional[int]     ):
							lowerCAmelCase__        =   FlaxViTModel(snake_case__     )
							lowerCAmelCase__        =   FlaxBertModel(snake_case__     )
							return vision_model, text_model




					def 						_SCREAMING_SNAKE_CASE						(	self						:     int     ):
							lowerCAmelCase__        =   FlaxViTModelTester(self     )
							lowerCAmelCase__        =   FlaxBertModelTester(self     )
							lowerCAmelCase__        =   vit_model_tester.prepare_config_and_inputs()
							lowerCAmelCase__        =   bert_model_tester.prepare_config_and_inputs()

							lowerCAmelCase__   ,					lowerCAmelCase__        =   vision_config_and_inputs

							lowerCAmelCase__   ,					lowerCAmelCase__   ,					lowerCAmelCase__   ,					lowerCAmelCase__        =   text_config_and_inputs

							# make sure that cross attention layers are added
							return {
							    "text_config": text_config,
							    "vision_config": vision_config,
							    "pixel_values": pixel_values,
							    "attention_mask": attention_mask,
							    "input_ids": input_ids,
							    "token_type_ids": token_type_ids,
							}

@require_torch
class       a_      (     __UpperCamelCase ,		unittest.TestCase			):




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Dict     ):
							lowerCAmelCase__        =   FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
							    """hf-internal-testing/tiny-random-clip"""  ,						"""hf-internal-testing/tiny-bert"""  ,						vision_from_pt=snake_case__  ,						text_from_pt=snake_case__  ,						)
							lowerCAmelCase__        =   13
							lowerCAmelCase__        =   floats_tensor(
							    [
							        batch_size,
							        model.config.vision_config.num_channels,
							        model.config.vision_config.image_size,
							        model.config.vision_config.image_size,
							    ]     )
							lowerCAmelCase__        =   ids_tensor([batch_size, 4]  ,						model.config.text_config.vocab_size     )
							lowerCAmelCase__        =   random_attention_mask([batch_size, 4]     )
							lowerCAmelCase__        =   {"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}

							return model, inputs




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[int]  ,						snake_case__						:     Dict  ,						snake_case__						:     str     ):
							lowerCAmelCase__        =   FlaxCLIPVisionModel(snake_case__     )
							lowerCAmelCase__        =   FlaxBertModel(snake_case__     )
							return vision_model, text_model




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[Any]     ):
							lowerCAmelCase__        =   FlaxCLIPVisionModelTester(self     )
							lowerCAmelCase__        =   FlaxBertModelTester(self     )
							lowerCAmelCase__        =   clip_model_tester.prepare_config_and_inputs()
							lowerCAmelCase__        =   bert_model_tester.prepare_config_and_inputs()

							lowerCAmelCase__   ,					lowerCAmelCase__        =   vision_config_and_inputs

							lowerCAmelCase__   ,					lowerCAmelCase__   ,					lowerCAmelCase__   ,					lowerCAmelCase__        =   text_config_and_inputs

							# make sure that cross attention layers are added
							return {
							    "text_config": text_config,
							    "vision_config": vision_config,
							    "pixel_values": pixel_values,
							    "attention_mask": attention_mask,
							    "input_ids": input_ids,
							    "token_type_ids": token_type_ids,
							}

@require_flax
@require_vision
class       a_      (     unittest.TestCase			):




					@slow
					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[Any]     ):
							lowerCAmelCase__        =   FlaxVisionTextDualEncoderModel.from_pretrained("""clip-italian/clip-italian"""  ,						logit_scale_init_value=1.0     )
							lowerCAmelCase__        =   VisionTextDualEncoderProcessor.from_pretrained("""clip-italian/clip-italian"""     )

							lowerCAmelCase__        =   Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""     )
							lowerCAmelCase__        =   processor(
							    text=["""una foto di un gatto""", """una foto di un cane"""]  ,						images=snake_case__  ,						padding=snake_case__  ,						return_tensors="""np"""     )

							lowerCAmelCase__        =   model(**snake_case__     )

							# verify the logits
							self.assertEqual(outputs.logits_per_image.shape  ,						(inputs.pixel_values.shape[0], inputs.input_ids.shape[0])     )
							self.assertEqual(
							    outputs.logits_per_text.shape  ,						(inputs.input_ids.shape[0], inputs.pixel_values.shape[0])  ,						)

							lowerCAmelCase__        =   np.array([[1.228_4727, 0.310_4122]]     )

							self.assertTrue(np.allclose(outputs.logits_per_image  ,						snake_case__  ,						atol=1E-3     )     )





"""simple docstring"""




import numpy as np
import torch
from torch.utils.data import DataLoader

from accelerate.utils.dataclasses import DistributedType

class       a_      :




					def __init__(	self						:     Optional[int]  ,						snake_case__						:     List[Any]=2  ,						snake_case__						:     Any=3  ,						snake_case__						:     Union[str, Any]=64  ,						snake_case__						:     Any=None     ):
							lowerCAmelCase__        =   np.random.default_rng(snake_case__     )
							lowerCAmelCase__        =   length
							lowerCAmelCase__        =   rng.normal(size=(length,)     ).astype(np.floataa     )
							lowerCAmelCase__        =   a * self.x + b + rng.normal(scale=0.1  ,						size=(length,)     ).astype(np.floataa     )




					def __len__(	self						:     Optional[Any]     ):
							return self.length




					def __getitem__(	self						:     List[str]  ,						snake_case__						:     Optional[int]     ):
							return {"x": self.x[i], "y": self.y[i]}

class       a_      (     torch.nn.Module			):




					def __init__(	self						:     List[str]  ,						snake_case__						:     str=0  ,						snake_case__						:     Dict=0  ,						snake_case__						:     Any=False     ):
							super().__init__()
							lowerCAmelCase__        =   torch.nn.Parameter(torch.tensor([2, 3]     ).float()     )
							lowerCAmelCase__        =   torch.nn.Parameter(torch.tensor([2, 3]     ).float()     )
							lowerCAmelCase__        =   True




					def 						_SCREAMING_SNAKE_CASE						(	self						:     int  ,						snake_case__						:     Any=None     ):
							if self.first_batch:
									print(F"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"""     )
									lowerCAmelCase__        =   False
							return x * self.a[0] + self.b[0]

class       a_      (     torch.nn.Module			):




					def __init__(	self						:     Any  ,						snake_case__						:     Union[str, Any]=0  ,						snake_case__						:     Union[str, Any]=0  ,						snake_case__						:     List[Any]=False     ):
							super().__init__()
							lowerCAmelCase__        =   torch.nn.Parameter(torch.tensor(snake_case__     ).float()     )
							lowerCAmelCase__        =   torch.nn.Parameter(torch.tensor(snake_case__     ).float()     )
							lowerCAmelCase__        =   True




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Tuple  ,						snake_case__						:     Optional[Any]=None     ):
							if self.first_batch:
									print(F"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"""     )
									lowerCAmelCase__        =   False
							return x * self.a + self.b







def 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__ = 16     ):





		"""simple docstring"""




		from datasets import load_dataset
		from transformers import AutoTokenizer

		lowerCAmelCase__        =   AutoTokenizer.from_pretrained("""bert-base-cased"""     )
		lowerCAmelCase__        =   {"""train""": """tests/test_samples/MRPC/train.csv""", """validation""": """tests/test_samples/MRPC/dev.csv"""}
		lowerCAmelCase__        =   load_dataset("""csv"""			,  data_files=lowerCamelCase__     )
		lowerCAmelCase__        =   datasets["""train"""].unique("""label"""     )

		lowerCAmelCase__        =   {v: i for i, v in enumerate(lowerCamelCase__     )}

		def tokenize_function(lowerCamelCase__     ):
				# max_length=None => use the model max length (it's actually the default)
				lowerCAmelCase__        =   tokenizer(
				    examples["""sentence1"""]			,  examples["""sentence2"""]			,  truncation=lowerCamelCase__			,  max_length=lowerCamelCase__			,  padding="""max_length"""     )
				if "label" in examples:
						lowerCAmelCase__        =   [label_to_id[l] for l in examples["""label"""]]
				return outputs

		# Apply the method we just defined to all the examples in all the splits of the dataset
		lowerCAmelCase__        =   datasets.map(
		    lowerCamelCase__			,  batched=lowerCamelCase__			,  remove_columns=["""sentence1""", """sentence2""", """label"""]			,  )

		def collate_fn(lowerCamelCase__     ):
				# On TPU it's best to pad everything to the same length or training will be very slow.
				if accelerator.distributed_type == DistributedType.TPU:
						return tokenizer.pad(lowerCamelCase__			,  padding="""max_length"""			,  max_length=128			,  return_tensors="""pt"""     )
				return tokenizer.pad(lowerCamelCase__			,  padding="""longest"""			,  return_tensors="""pt"""     )

		# Instantiate dataloaders.
		lowerCAmelCase__        =   DataLoader(tokenized_datasets["""train"""]			,  shuffle=lowerCamelCase__			,  collate_fn=lowerCamelCase__			,  batch_size=2     )
		lowerCAmelCase__        =   DataLoader(tokenized_datasets["""validation"""]			,  shuffle=lowerCamelCase__			,  collate_fn=lowerCamelCase__			,  batch_size=1     )

		return train_dataloader, eval_dataloader




