

import unittest

import numpy as np

from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
    expand_dims,
    flatten_dict,
    is_flax_available,
    is_tf_available,
    is_torch_available,
    reshape,
    squeeze,
    transpose,
)


if is_flax_available():
     import jax.numpy as jnp

if is_tf_available():
     import tensorflow as tf

if is_torch_available():
     import torch





class __SCREAMING_SNAKE_CASE     (	unittest.TestCase		):






    def 						__lowerCAmelCase    (       self )   ->					Union[str, Any]:
       __SCREAMING_SNAKE_CASE       =				{
           "task_specific_params": {
               "summarization": {"length_penalty": 1.0, "max_length": 1_28, "min_length": 12, "num_beams": 4},
               "summarization_cnn": {"length_penalty": 2.0, "max_length": 1_42, "min_length": 56, "num_beams": 4},
               "summarization_xsum": {"length_penalty": 1.0, "max_length": 62, "min_length": 11, "num_beams": 6},
           }
       }
       __SCREAMING_SNAKE_CASE       =				{
           "task_specific_params.summarization.length_penalty": 1.0,
           "task_specific_params.summarization.max_length": 1_28,
           "task_specific_params.summarization.min_length": 12,
           "task_specific_params.summarization.num_beams": 4,
           "task_specific_params.summarization_cnn.length_penalty": 2.0,
           "task_specific_params.summarization_cnn.max_length": 1_42,
           "task_specific_params.summarization_cnn.min_length": 56,
           "task_specific_params.summarization_cnn.num_beams": 4,
           "task_specific_params.summarization_xsum.length_penalty": 1.0,
           "task_specific_params.summarization_xsum.max_length": 62,
           "task_specific_params.summarization_xsum.min_length": 11,
           "task_specific_params.summarization_xsum.num_beams": 6,
       }

       self.assertEqual(flatten_dict(lowerCamelCase_ ),		lowerCamelCase_ )






    def 						__lowerCAmelCase    (       self )   ->					List[str]:
       __SCREAMING_SNAKE_CASE       =				np.random.randn(3,		4 )
       self.assertTrue(np.allclose(transpose(lowerCamelCase_ ),		x.transpose() ) )

       __SCREAMING_SNAKE_CASE       =				np.random.randn(3,		4,		5 )
       self.assertTrue(np.allclose(transpose(lowerCamelCase_,		axes=(1, 2, 0) ),		x.transpose((1, 2, 0) ) ) )






    @require_torch
    def 						__lowerCAmelCase    (       self )   ->					Union[str, Any]:
       __SCREAMING_SNAKE_CASE       =				np.random.randn(3,		4 )
       __SCREAMING_SNAKE_CASE       =				torch.tensor(lowerCamelCase_ )
       self.assertTrue(np.allclose(transpose(lowerCamelCase_ ),		transpose(lowerCamelCase_ ).numpy() ) )

       __SCREAMING_SNAKE_CASE       =				np.random.randn(3,		4,		5 )
       __SCREAMING_SNAKE_CASE       =				torch.tensor(lowerCamelCase_ )
       self.assertTrue(np.allclose(transpose(lowerCamelCase_,		axes=(1, 2, 0) ),		transpose(lowerCamelCase_,		axes=(1, 2, 0) ).numpy() ) )






    @require_tf
    def 						__lowerCAmelCase    (       self )   ->					List[str]:
       __SCREAMING_SNAKE_CASE       =				np.random.randn(3,		4 )
       __SCREAMING_SNAKE_CASE       =				tf.constant(lowerCamelCase_ )
       self.assertTrue(np.allclose(transpose(lowerCamelCase_ ),		transpose(lowerCamelCase_ ).numpy() ) )

       __SCREAMING_SNAKE_CASE       =				np.random.randn(3,		4,		5 )
       __SCREAMING_SNAKE_CASE       =				tf.constant(lowerCamelCase_ )
       self.assertTrue(np.allclose(transpose(lowerCamelCase_,		axes=(1, 2, 0) ),		transpose(lowerCamelCase_,		axes=(1, 2, 0) ).numpy() ) )






    @require_flax
    def 						__lowerCAmelCase    (       self )   ->					Any:
       __SCREAMING_SNAKE_CASE       =				np.random.randn(3,		4 )
       __SCREAMING_SNAKE_CASE       =				jnp.array(lowerCamelCase_ )
       self.assertTrue(np.allclose(transpose(lowerCamelCase_ ),		np.asarray(transpose(lowerCamelCase_ ) ) ) )

       __SCREAMING_SNAKE_CASE       =				np.random.randn(3,		4,		5 )
       __SCREAMING_SNAKE_CASE       =				jnp.array(lowerCamelCase_ )
       self.assertTrue(np.allclose(transpose(lowerCamelCase_,		axes=(1, 2, 0) ),		np.asarray(transpose(lowerCamelCase_,		axes=(1, 2, 0) ) ) ) )






    def 						__lowerCAmelCase    (       self )   ->					List[Any]:
       __SCREAMING_SNAKE_CASE       =				np.random.randn(3,		4 )
       self.assertTrue(np.allclose(reshape(lowerCamelCase_,		(4, 3) ),		np.reshape(lowerCamelCase_,		(4, 3) ) ) )

       __SCREAMING_SNAKE_CASE       =				np.random.randn(3,		4,		5 )
       self.assertTrue(np.allclose(reshape(lowerCamelCase_,		(12, 5) ),		np.reshape(lowerCamelCase_,		(12, 5) ) ) )






    @require_torch
    def 						__lowerCAmelCase    (       self )   ->					Tuple:
       __SCREAMING_SNAKE_CASE       =				np.random.randn(3,		4 )
       __SCREAMING_SNAKE_CASE       =				torch.tensor(lowerCamelCase_ )
       self.assertTrue(np.allclose(reshape(lowerCamelCase_,		(4, 3) ),		reshape(lowerCamelCase_,		(4, 3) ).numpy() ) )

       __SCREAMING_SNAKE_CASE       =				np.random.randn(3,		4,		5 )
       __SCREAMING_SNAKE_CASE       =				torch.tensor(lowerCamelCase_ )
       self.assertTrue(np.allclose(reshape(lowerCamelCase_,		(12, 5) ),		reshape(lowerCamelCase_,		(12, 5) ).numpy() ) )






    @require_tf
    def 						__lowerCAmelCase    (       self )   ->					int:
       __SCREAMING_SNAKE_CASE       =				np.random.randn(3,		4 )
       __SCREAMING_SNAKE_CASE       =				tf.constant(lowerCamelCase_ )
       self.assertTrue(np.allclose(reshape(lowerCamelCase_,		(4, 3) ),		reshape(lowerCamelCase_,		(4, 3) ).numpy() ) )

       __SCREAMING_SNAKE_CASE       =				np.random.randn(3,		4,		5 )
       __SCREAMING_SNAKE_CASE       =				tf.constant(lowerCamelCase_ )
       self.assertTrue(np.allclose(reshape(lowerCamelCase_,		(12, 5) ),		reshape(lowerCamelCase_,		(12, 5) ).numpy() ) )






    @require_flax
    def 						__lowerCAmelCase    (       self )   ->					str:
       __SCREAMING_SNAKE_CASE       =				np.random.randn(3,		4 )
       __SCREAMING_SNAKE_CASE       =				jnp.array(lowerCamelCase_ )
       self.assertTrue(np.allclose(reshape(lowerCamelCase_,		(4, 3) ),		np.asarray(reshape(lowerCamelCase_,		(4, 3) ) ) ) )

       __SCREAMING_SNAKE_CASE       =				np.random.randn(3,		4,		5 )
       __SCREAMING_SNAKE_CASE       =				jnp.array(lowerCamelCase_ )
       self.assertTrue(np.allclose(reshape(lowerCamelCase_,		(12, 5) ),		np.asarray(reshape(lowerCamelCase_,		(12, 5) ) ) ) )






    def 						__lowerCAmelCase    (       self )   ->					Any:
       __SCREAMING_SNAKE_CASE       =				np.random.randn(1,		3,		4 )
       self.assertTrue(np.allclose(squeeze(lowerCamelCase_ ),		np.squeeze(lowerCamelCase_ ) ) )

       __SCREAMING_SNAKE_CASE       =				np.random.randn(1,		4,		1,		5 )
       self.assertTrue(np.allclose(squeeze(lowerCamelCase_,		axis=2 ),		np.squeeze(lowerCamelCase_,		axis=2 ) ) )






    @require_torch
    def 						__lowerCAmelCase    (       self )   ->					Dict:
       __SCREAMING_SNAKE_CASE       =				np.random.randn(1,		3,		4 )
       __SCREAMING_SNAKE_CASE       =				torch.tensor(lowerCamelCase_ )
       self.assertTrue(np.allclose(squeeze(lowerCamelCase_ ),		squeeze(lowerCamelCase_ ).numpy() ) )

       __SCREAMING_SNAKE_CASE       =				np.random.randn(1,		4,		1,		5 )
       __SCREAMING_SNAKE_CASE       =				torch.tensor(lowerCamelCase_ )
       self.assertTrue(np.allclose(squeeze(lowerCamelCase_,		axis=2 ),		squeeze(lowerCamelCase_,		axis=2 ).numpy() ) )






    @require_tf
    def 						__lowerCAmelCase    (       self )   ->					Dict:
       __SCREAMING_SNAKE_CASE       =				np.random.randn(1,		3,		4 )
       __SCREAMING_SNAKE_CASE       =				tf.constant(lowerCamelCase_ )
       self.assertTrue(np.allclose(squeeze(lowerCamelCase_ ),		squeeze(lowerCamelCase_ ).numpy() ) )

       __SCREAMING_SNAKE_CASE       =				np.random.randn(1,		4,		1,		5 )
       __SCREAMING_SNAKE_CASE       =				tf.constant(lowerCamelCase_ )
       self.assertTrue(np.allclose(squeeze(lowerCamelCase_,		axis=2 ),		squeeze(lowerCamelCase_,		axis=2 ).numpy() ) )






    @require_flax
    def 						__lowerCAmelCase    (       self )   ->					str:
       __SCREAMING_SNAKE_CASE       =				np.random.randn(1,		3,		4 )
       __SCREAMING_SNAKE_CASE       =				jnp.array(lowerCamelCase_ )
       self.assertTrue(np.allclose(squeeze(lowerCamelCase_ ),		np.asarray(squeeze(lowerCamelCase_ ) ) ) )

       __SCREAMING_SNAKE_CASE       =				np.random.randn(1,		4,		1,		5 )
       __SCREAMING_SNAKE_CASE       =				jnp.array(lowerCamelCase_ )
       self.assertTrue(np.allclose(squeeze(lowerCamelCase_,		axis=2 ),		np.asarray(squeeze(lowerCamelCase_,		axis=2 ) ) ) )






    def 						__lowerCAmelCase    (       self )   ->					Dict:
       __SCREAMING_SNAKE_CASE       =				np.random.randn(3,		4 )
       self.assertTrue(np.allclose(expand_dims(lowerCamelCase_,		axis=1 ),		np.expand_dims(lowerCamelCase_,		axis=1 ) ) )






    @require_torch
    def 						__lowerCAmelCase    (       self )   ->					Dict:
       __SCREAMING_SNAKE_CASE       =				np.random.randn(3,		4 )
       __SCREAMING_SNAKE_CASE       =				torch.tensor(lowerCamelCase_ )
       self.assertTrue(np.allclose(expand_dims(lowerCamelCase_,		axis=1 ),		expand_dims(lowerCamelCase_,		axis=1 ).numpy() ) )






    @require_tf
    def 						__lowerCAmelCase    (       self )   ->					Optional[Any]:
       __SCREAMING_SNAKE_CASE       =				np.random.randn(3,		4 )
       __SCREAMING_SNAKE_CASE       =				tf.constant(lowerCamelCase_ )
       self.assertTrue(np.allclose(expand_dims(lowerCamelCase_,		axis=1 ),		expand_dims(lowerCamelCase_,		axis=1 ).numpy() ) )






    @require_flax
    def 						__lowerCAmelCase    (       self )   ->					Optional[int]:
       __SCREAMING_SNAKE_CASE       =				np.random.randn(3,		4 )
       __SCREAMING_SNAKE_CASE       =				jnp.array(lowerCamelCase_ )
       self.assertTrue(np.allclose(expand_dims(lowerCamelCase_,		axis=1 ),		np.asarray(expand_dims(lowerCamelCase_,		axis=1 ) ) ) )





'''simple docstring'''






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

from accelerate.utils.dataclasses import DistributedType




class 						a__							:





							'''simple docstring'''






							def __init__(						self   ,       lowerCamelCase_=2   ,       lowerCamelCase_=3   ,       lowerCamelCase_=64   ,       lowerCamelCase_=None  )			->				Dict:
														lowerCAmelCase__			  =      np.random.default_rng(lowerCamelCase_  )
														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  )			->				Any:
														return self.length







							def __getitem__(						self   ,       lowerCamelCase_  )			->				List[str]:
														return {"x": self.x[i], "y": self.y[i]}




class 						a__							(				torch.nn.Module    ):





							'''simple docstring'''






							def __init__(						self   ,       lowerCamelCase_=0   ,       lowerCamelCase_=0   ,       lowerCamelCase_=False  )			->				List[Any]:
														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   ,       lowerCamelCase_=None  )			->				Optional[Any]:
														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    ):





							'''simple docstring'''






							def __init__(						self   ,       lowerCamelCase_=0   ,       lowerCamelCase_=0   ,       lowerCamelCase_=False  )			->				Any:
														super().__init__()
														lowerCAmelCase__			  =      torch.nn.Parameter(torch.tensor(lowerCamelCase_  ).float()  )
														lowerCAmelCase__			  =      torch.nn.Parameter(torch.tensor(lowerCamelCase_  ).float()  )
														lowerCAmelCase__			  =      True







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_=None  )			->				Any:
														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    _snake_case  (	A					,       A = 16			)		->       Any:
							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=A			)
							lowerCAmelCase__			  =      datasets['''train'''].unique('''label'''			)

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

							def tokenize_function(A			):
														# max_length=None => use the model max length (it's actually the default)
														lowerCAmelCase__			  =      tokenizer(
														    examples['''sentence1''']					,       examples['''sentence2''']					,       truncation=A					,       max_length=A					,       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(
							    A					,       batched=A					,       remove_columns=['''sentence1''', '''sentence2''', '''label''']					,       )

							def collate_fn(A			):
														# 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(A					,       padding='''max_length'''					,       max_length=128					,       return_tensors='''pt'''			)
														return tokenizer.pad(A					,       padding='''longest'''					,       return_tensors='''pt'''			)

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

							return train_dataloader, eval_dataloader