





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


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import ChineseCLIPImageProcessor






class 					lowercase__ (			unittest.TestCase       ):

   '''simple docstring'''


   def __init__(					self,	__magic_name__,	__magic_name__=7,	__magic_name__=3,	__magic_name__=18,	__magic_name__=30,	__magic_name__=400,	__magic_name__=True,	__magic_name__=None,	__magic_name__=True,	__magic_name__=None,	__magic_name__=True,	__magic_name__=[0.4814_5466, 0.457_8275, 0.4082_1073],	__magic_name__=[0.2686_2954, 0.2613_0258, 0.2757_7711],	__magic_name__=True,	)				->   List[Any]:






       """simple docstring"""
       UpperCamelCase__							:       Optional[Any]      =   size if size is not None else {'''height''': 224, '''width''': 224}
       UpperCamelCase__							:       Union[str, Any]      =   crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
       UpperCamelCase__							:       Tuple      =   parent
       UpperCamelCase__							:       Dict      =   batch_size
       UpperCamelCase__							:       List[str]      =   num_channels
       UpperCamelCase__							:       int      =   image_size
       UpperCamelCase__							:       List[str]      =   min_resolution
       UpperCamelCase__							:       List[Any]      =   max_resolution
       UpperCamelCase__							:       Dict      =   do_resize
       UpperCamelCase__							:       int      =   size
       UpperCamelCase__							:       Optional[int]      =   do_center_crop
       UpperCamelCase__							:       str      =   crop_size
       UpperCamelCase__							:       Union[str, Any]      =   do_normalize
       UpperCamelCase__							:       Dict      =   image_mean
       UpperCamelCase__							:       Union[str, Any]      =   image_std
       UpperCamelCase__							:       Optional[Any]      =   do_convert_rgb




   def    UpperCamelCase__						(					self							)				->   List[str]:






       """simple docstring"""
       return {
           "do_resize": self.do_resize,
           "size": self.size,
           "do_center_crop": self.do_center_crop,
           "crop_size": self.crop_size,
           "do_normalize": self.do_normalize,
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "do_convert_rgb": self.do_convert_rgb,
       }




   def    UpperCamelCase__						(					self,	__magic_name__=False,	__magic_name__=False,	__magic_name__=False							)				->   int:






       """simple docstring"""
       assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

       if equal_resolution:
           UpperCamelCase__							:       int      =   []
           for i in range(self.batch_size							):
               image_inputs.append(
                   np.random.randint(
                       255,	size=(self.num_channels, self.max_resolution, self.max_resolution),	dtype=np.uinta							)							)
       else:
           UpperCamelCase__							:       Dict      =   []
           for i in range(self.batch_size							):
               UpperCamelCase__     ,UpperCamelCase__							:       Dict      =   np.random.choice(np.arange(self.min_resolution,	self.max_resolution							),	2							)
               image_inputs.append(np.random.randint(255,	size=(self.num_channels, width, height),	dtype=np.uinta							)							)

       if not numpify and not torchify:
           # PIL expects the channel dimension as last dimension
           UpperCamelCase__							:       Dict      =   [Image.fromarray(np.moveaxis(__magic_name__,	0,	-1							)							) for x in image_inputs]

       if torchify:
           UpperCamelCase__							:       List[Any]      =   [torch.from_numpy(__magic_name__							) for x in image_inputs]

       return image_inputs






@require_torch
@require_vision
class 					lowercase__ (			__lowerCamelCase    ,       unittest.TestCase       ):

   '''simple docstring'''


   a		:							Dict     	=	ChineseCLIPImageProcessor if is_vision_available() else None




   def    UpperCamelCase__						(					self							)				->   Dict:






       """simple docstring"""
       UpperCamelCase__							:       int      =   ChineseCLIPImageProcessingTester(self,	do_center_crop=__magic_name__							)




   @property
   def    UpperCamelCase__						(					self							)				->   Dict:






       """simple docstring"""
       return self.image_processor_tester.prepare_image_processor_dict()




   def    UpperCamelCase__						(					self							)				->   Optional[Any]:






       """simple docstring"""
       UpperCamelCase__							:       List[Any]      =   self.image_processing_class(**self.image_processor_dict							)
       self.assertTrue(hasattr(__magic_name__,	'''do_resize'''							)							)
       self.assertTrue(hasattr(__magic_name__,	'''size'''							)							)
       self.assertTrue(hasattr(__magic_name__,	'''do_center_crop'''							)							)
       self.assertTrue(hasattr(__magic_name__,	'''center_crop'''							)							)
       self.assertTrue(hasattr(__magic_name__,	'''do_normalize'''							)							)
       self.assertTrue(hasattr(__magic_name__,	'''image_mean'''							)							)
       self.assertTrue(hasattr(__magic_name__,	'''image_std'''							)							)
       self.assertTrue(hasattr(__magic_name__,	'''do_convert_rgb'''							)							)




   def    UpperCamelCase__						(					self							)				->   List[str]:






       """simple docstring"""
       UpperCamelCase__							:       List[Any]      =   self.image_processing_class.from_dict(self.image_processor_dict							)
       self.assertEqual(image_processor.size,	{'''height''': 224, '''width''': 224}							)
       self.assertEqual(image_processor.crop_size,	{'''height''': 18, '''width''': 18}							)

       UpperCamelCase__							:       List[Any]      =   self.image_processing_class.from_dict(self.image_processor_dict,	size=42,	crop_size=84							)
       self.assertEqual(image_processor.size,	{'''shortest_edge''': 42}							)
       self.assertEqual(image_processor.crop_size,	{'''height''': 84, '''width''': 84}							)




   def    UpperCamelCase__						(					self							)				->   Optional[Any]:






       """simple docstring"""
       pass




   def    UpperCamelCase__						(					self							)				->   int:






       """simple docstring"""
       # Initialize image_processing
       UpperCamelCase__							:       str      =   self.image_processing_class(**self.image_processor_dict							)
       # create random PIL images
       UpperCamelCase__							:       str      =   self.image_processor_tester.prepare_inputs(equal_resolution=__magic_name__							)
       for image in image_inputs:
           self.assertIsInstance(__magic_name__,	Image.Image							)

       # Test not batched input
       UpperCamelCase__							:       Union[str, Any]      =   image_processing(image_inputs[0],	return_tensors='''pt'''							).pixel_values
       self.assertEqual(
           encoded_images.shape,	(
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           ),	)

       # Test batched
       UpperCamelCase__							:       Any      =   image_processing(__magic_name__,	return_tensors='''pt'''							).pixel_values
       self.assertEqual(
           encoded_images.shape,	(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           ),	)




   def    UpperCamelCase__						(					self							)				->   List[Any]:






       """simple docstring"""
       # Initialize image_processing
       UpperCamelCase__							:       Tuple      =   self.image_processing_class(**self.image_processor_dict							)
       # create random numpy tensors
       UpperCamelCase__							:       List[Any]      =   self.image_processor_tester.prepare_inputs(equal_resolution=__magic_name__,	numpify=__magic_name__							)
       for image in image_inputs:
           self.assertIsInstance(__magic_name__,	np.ndarray							)

       # Test not batched input
       UpperCamelCase__							:       Optional[int]      =   image_processing(image_inputs[0],	return_tensors='''pt'''							).pixel_values
       self.assertEqual(
           encoded_images.shape,	(
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           ),	)

       # Test batched
       UpperCamelCase__							:       Tuple      =   image_processing(__magic_name__,	return_tensors='''pt'''							).pixel_values
       self.assertEqual(
           encoded_images.shape,	(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           ),	)




   def    UpperCamelCase__						(					self							)				->   Any:






       """simple docstring"""
       # Initialize image_processing
       UpperCamelCase__							:       List[Any]      =   self.image_processing_class(**self.image_processor_dict							)
       # create random PyTorch tensors
       UpperCamelCase__							:       List[str]      =   self.image_processor_tester.prepare_inputs(equal_resolution=__magic_name__,	torchify=__magic_name__							)
       for image in image_inputs:
           self.assertIsInstance(__magic_name__,	torch.Tensor							)

       # Test not batched input
       UpperCamelCase__							:       int      =   image_processing(image_inputs[0],	return_tensors='''pt'''							).pixel_values
       self.assertEqual(
           encoded_images.shape,	(
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           ),	)

       # Test batched
       UpperCamelCase__							:       List[Any]      =   image_processing(__magic_name__,	return_tensors='''pt'''							).pixel_values
       self.assertEqual(
           encoded_images.shape,	(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           ),	)






@require_torch
@require_vision
class 					lowercase__ (			__lowerCamelCase    ,       unittest.TestCase       ):

   '''simple docstring'''


   a		:							List[str]     	=	ChineseCLIPImageProcessor if is_vision_available() else None




   def    UpperCamelCase__						(					self							)				->   str:






       """simple docstring"""
       UpperCamelCase__							:       List[str]      =   ChineseCLIPImageProcessingTester(self,	num_channels=4,	do_center_crop=__magic_name__							)
       UpperCamelCase__							:       str      =   3




   @property
   def    UpperCamelCase__						(					self							)				->   str:






       """simple docstring"""
       return self.image_processor_tester.prepare_image_processor_dict()




   def    UpperCamelCase__						(					self							)				->   List[str]:






       """simple docstring"""
       UpperCamelCase__							:       Dict      =   self.image_processing_class(**self.image_processor_dict							)
       self.assertTrue(hasattr(__magic_name__,	'''do_resize'''							)							)
       self.assertTrue(hasattr(__magic_name__,	'''size'''							)							)
       self.assertTrue(hasattr(__magic_name__,	'''do_center_crop'''							)							)
       self.assertTrue(hasattr(__magic_name__,	'''center_crop'''							)							)
       self.assertTrue(hasattr(__magic_name__,	'''do_normalize'''							)							)
       self.assertTrue(hasattr(__magic_name__,	'''image_mean'''							)							)
       self.assertTrue(hasattr(__magic_name__,	'''image_std'''							)							)
       self.assertTrue(hasattr(__magic_name__,	'''do_convert_rgb'''							)							)




   def    UpperCamelCase__						(					self							)				->   int:






       """simple docstring"""
       pass




   def    UpperCamelCase__						(					self							)				->   List[Any]:






       """simple docstring"""
       # Initialize image_processing
       UpperCamelCase__							:       str      =   self.image_processing_class(**self.image_processor_dict							)
       # create random PIL images
       UpperCamelCase__							:       int      =   self.image_processor_tester.prepare_inputs(equal_resolution=__magic_name__							)
       for image in image_inputs:
           self.assertIsInstance(__magic_name__,	Image.Image							)

       # Test not batched input
       UpperCamelCase__							:       List[Any]      =   image_processing(image_inputs[0],	return_tensors='''pt'''							).pixel_values
       self.assertEqual(
           encoded_images.shape,	(
               1,
               self.expected_encoded_image_num_channels,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           ),	)

       # Test batched
       UpperCamelCase__							:       List[Any]      =   image_processing(__magic_name__,	return_tensors='''pt'''							).pixel_values
       self.assertEqual(
           encoded_images.shape,	(
               self.image_processor_tester.batch_size,
               self.expected_encoded_image_num_channels,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           ),	)








import random
import unittest

import numpy as np

import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax


if is_flax_available():
  import os

  import jax.numpy as jnp
  from jax import jit

  from transformers import AutoTokenizer, FlaxAutoModelForCausalLM
  from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model

  UpperCAmelCase_  							=				'0.12'  # assumed parallelism: 8


if is_torch_available():
  import torch




def 	lowerCAmelCase_					(						__UpperCAmelCase:    Dict	,     __UpperCAmelCase:    Tuple	,     __UpperCAmelCase:    Optional[Any]=None   )   ->			Union[str, Any]:
    if rng is None:
        UpperCamelCase__							:       Tuple      =   random.Random()

    UpperCamelCase__							:       List[str]      =   1
    for dim in shape:
        total_dims *= dim

    UpperCamelCase__							:       Tuple      =   []
    for _ in range(__UpperCAmelCase   ):
        values.append(rng.randint(0	,     vocab_size - 1   )   )

    UpperCamelCase__							:       Any      =   np.array(__UpperCAmelCase	,     dtype=jnp.intaa   ).reshape(__UpperCAmelCase   )

    return output




def 	lowerCAmelCase_					(						__UpperCAmelCase:    Optional[int]	,     __UpperCAmelCase:    Optional[int]=None   )   ->			Any:
    UpperCamelCase__							:       Any      =   ids_tensor(__UpperCAmelCase	,     vocab_size=2	,     rng=__UpperCAmelCase   )
    # make sure that at least one token is attended to for each batch
    UpperCamelCase__							:       Tuple      =   1
    return attn_mask






@require_flax
class 					lowercase__ :

   '''simple docstring'''


   a		:							Optional[int]     	=	None
   a		:							Union[str, Any]     	=	()




   def    UpperCamelCase__						(					self							)				->   str:






       """simple docstring"""
       UpperCamelCase__     ,UpperCamelCase__							:       Tuple      =   self.model_tester.prepare_config_and_inputs_for_common()

       # cut to half length & take max batch_size 3
       UpperCamelCase__							:       str      =   2
       UpperCamelCase__							:       Dict      =   inputs['''input_ids'''].shape[-1] // 2
       UpperCamelCase__							:       int      =   inputs['''input_ids'''][:max_batch_size, :sequence_length]

       UpperCamelCase__							:       Tuple      =   jnp.ones_like(__magic_name__							)
       UpperCamelCase__							:       Any      =   attention_mask[:max_batch_size, :sequence_length]

       # generate max 5 tokens
       UpperCamelCase__							:       List[str]      =   input_ids.shape[-1] + 5
       if config.eos_token_id is not None and config.pad_token_id is None:
           # hack to allow generate for models such as GPT2 as is done in `generate()`
           UpperCamelCase__							:       Tuple      =   config.eos_token_id
       return config, input_ids, attention_mask, max_length




   @is_pt_flax_cross_test
   def    UpperCamelCase__						(					self							)				->   List[str]:






       """simple docstring"""
       UpperCamelCase__     ,UpperCamelCase__     ,UpperCamelCase__     ,UpperCamelCase__							:       Optional[int]      =   self._get_input_ids_and_config()
       UpperCamelCase__							:       Optional[int]      =   False
       UpperCamelCase__							:       Optional[int]      =   max_length
       UpperCamelCase__							:       Dict      =   0

       for model_class in self.all_generative_model_classes:
           UpperCamelCase__							:       str      =   model_class(__magic_name__							)

           UpperCamelCase__							:       int      =   model_class.__name__[4:]  # Skip the "Flax" at the beginning
           UpperCamelCase__							:       List[str]      =   getattr(__magic_name__,	__magic_name__							)
           UpperCamelCase__							:       List[Any]      =   pt_model_class(__magic_name__							).eval()
           UpperCamelCase__							:       int      =   load_flax_weights_in_pytorch_model(__magic_name__,	flax_model.params							)

           UpperCamelCase__							:       Optional[int]      =   flax_model.generate(__magic_name__							).sequences
           UpperCamelCase__							:       Union[str, Any]      =   pt_model.generate(torch.tensor(__magic_name__,	dtype=torch.long							)							)

           if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
               UpperCamelCase__							:       Optional[Any]      =   flax_generation_outputs[:, : pt_generation_outputs.shape[-1]]

           self.assertListEqual(pt_generation_outputs.numpy().tolist(),	flax_generation_outputs.tolist()							)




   def    UpperCamelCase__						(					self							)				->   Optional[int]:






       """simple docstring"""
       UpperCamelCase__     ,UpperCamelCase__     ,UpperCamelCase__     ,UpperCamelCase__							:       List[Any]      =   self._get_input_ids_and_config()
       UpperCamelCase__							:       Dict      =   False
       UpperCamelCase__							:       List[str]      =   max_length

       for model_class in self.all_generative_model_classes:
           UpperCamelCase__							:       Dict      =   model_class(__magic_name__							)

           UpperCamelCase__							:       List[Any]      =   model.generate(__magic_name__							).sequences
           self.assertEqual(generation_outputs.shape[-1],	__magic_name__							)

           UpperCamelCase__							:       int      =   jit(model.generate							)
           UpperCamelCase__							:       Optional[Any]      =   jit_generate(__magic_name__							).sequences

           self.assertListEqual(generation_outputs.tolist(),	jit_generation_outputs.tolist()							)




   def    UpperCamelCase__						(					self							)				->   Optional[int]:






       """simple docstring"""
       UpperCamelCase__     ,UpperCamelCase__     ,UpperCamelCase__     ,UpperCamelCase__							:       Union[str, Any]      =   self._get_input_ids_and_config()
       UpperCamelCase__							:       Optional[int]      =   True
       UpperCamelCase__							:       List[str]      =   max_length

       for model_class in self.all_generative_model_classes:
           UpperCamelCase__							:       Dict      =   model_class(__magic_name__							)

           UpperCamelCase__							:       Dict      =   model.generate(__magic_name__							).sequences
           self.assertEqual(generation_outputs.shape[-1],	__magic_name__							)

           UpperCamelCase__							:       Optional[Any]      =   jit(model.generate							)
           UpperCamelCase__							:       Optional[int]      =   jit_generate(__magic_name__							).sequences

           self.assertListEqual(generation_outputs.tolist(),	jit_generation_outputs.tolist()							)




   def    UpperCamelCase__						(					self							)				->   Dict:






       """simple docstring"""
       UpperCamelCase__     ,UpperCamelCase__     ,UpperCamelCase__     ,UpperCamelCase__							:       Optional[Any]      =   self._get_input_ids_and_config()
       UpperCamelCase__							:       List[str]      =   False
       UpperCamelCase__							:       Dict      =   max_length
       UpperCamelCase__							:       str      =   2

       for model_class in self.all_generative_model_classes:
           UpperCamelCase__							:       Optional[int]      =   model_class(__magic_name__							)

           UpperCamelCase__							:       List[Any]      =   model.generate(__magic_name__							).sequences
           self.assertEqual(generation_outputs.shape[-1],	__magic_name__							)

           UpperCamelCase__							:       str      =   jit(model.generate							)
           UpperCamelCase__							:       int      =   jit_generate(__magic_name__							).sequences

           self.assertListEqual(generation_outputs.tolist(),	jit_generation_outputs.tolist()							)




   def    UpperCamelCase__						(					self							)				->   Union[str, Any]:






       """simple docstring"""
       UpperCamelCase__     ,UpperCamelCase__     ,UpperCamelCase__     ,UpperCamelCase__							:       Any      =   self._get_input_ids_and_config()
       UpperCamelCase__							:       Tuple      =   False
       UpperCamelCase__							:       Union[str, Any]      =   max_length
       UpperCamelCase__							:       Optional[Any]      =   2
       UpperCamelCase__							:       List[Any]      =   2

       for model_class in self.all_generative_model_classes:
           UpperCamelCase__							:       List[str]      =   model_class(__magic_name__							)

           UpperCamelCase__							:       List[str]      =   model.generate(__magic_name__							).sequences
           self.assertEqual(generation_outputs.shape[0],	input_ids.shape[0] * config.num_return_sequences							)




   def    UpperCamelCase__						(					self							)				->   Dict:






       """simple docstring"""
       UpperCamelCase__     ,UpperCamelCase__     ,UpperCamelCase__     ,UpperCamelCase__							:       Optional[int]      =   self._get_input_ids_and_config()
       UpperCamelCase__							:       List[str]      =   True
       UpperCamelCase__							:       Any      =   max_length
       UpperCamelCase__							:       Union[str, Any]      =   0.8
       UpperCamelCase__							:       Dict      =   10
       UpperCamelCase__							:       Any      =   0.3
       UpperCamelCase__							:       str      =   1
       UpperCamelCase__							:       Union[str, Any]      =   8
       UpperCamelCase__							:       Union[str, Any]      =   9

       for model_class in self.all_generative_model_classes:
           UpperCamelCase__							:       int      =   model_class(__magic_name__							)

           UpperCamelCase__							:       Union[str, Any]      =   model.generate(__magic_name__							).sequences
           self.assertEqual(generation_outputs.shape[-1],	__magic_name__							)

           UpperCamelCase__							:       Any      =   jit(model.generate							)
           UpperCamelCase__							:       List[Any]      =   jit_generate(__magic_name__							).sequences

           self.assertListEqual(generation_outputs.tolist(),	jit_generation_outputs.tolist()							)




   def    UpperCamelCase__						(					self							)				->   List[Any]:






       """simple docstring"""
       UpperCamelCase__     ,UpperCamelCase__     ,UpperCamelCase__     ,UpperCamelCase__							:       Optional[int]      =   self._get_input_ids_and_config()
       UpperCamelCase__							:       Any      =   max_length
       UpperCamelCase__							:       str      =   1
       UpperCamelCase__							:       Dict      =   8
       UpperCamelCase__							:       List[Any]      =   9

       for model_class in self.all_generative_model_classes:
           UpperCamelCase__							:       Union[str, Any]      =   model_class(__magic_name__							)

           UpperCamelCase__							:       Optional[int]      =   model.generate(__magic_name__							).sequences
           self.assertEqual(generation_outputs.shape[-1],	__magic_name__							)

           UpperCamelCase__							:       Dict      =   jit(model.generate							)
           UpperCamelCase__							:       int      =   jit_generate(__magic_name__							).sequences

           self.assertListEqual(generation_outputs.tolist(),	jit_generation_outputs.tolist()							)




   def    UpperCamelCase__						(					self							)				->   Optional[int]:






       """simple docstring"""
       UpperCamelCase__     ,UpperCamelCase__     ,UpperCamelCase__     ,UpperCamelCase__							:       int      =   self._get_input_ids_and_config()
       UpperCamelCase__							:       Dict      =   max_length
       UpperCamelCase__							:       List[Any]      =   2
       UpperCamelCase__							:       Dict      =   1
       UpperCamelCase__							:       int      =   8
       UpperCamelCase__							:       List[Any]      =   9

       for model_class in self.all_generative_model_classes:
           UpperCamelCase__							:       Union[str, Any]      =   model_class(__magic_name__							)

           UpperCamelCase__							:       Union[str, Any]      =   model.generate(__magic_name__							).sequences
           self.assertEqual(generation_outputs.shape[-1],	__magic_name__							)

           UpperCamelCase__							:       Optional[Any]      =   jit(model.generate							)
           UpperCamelCase__							:       Any      =   jit_generate(__magic_name__							).sequences

           self.assertListEqual(generation_outputs.tolist(),	jit_generation_outputs.tolist()							)




   def    UpperCamelCase__						(					self							)				->   Union[str, Any]:






       """simple docstring"""
       UpperCamelCase__     ,UpperCamelCase__     ,UpperCamelCase__     ,UpperCamelCase__							:       Optional[Any]      =   self._get_input_ids_and_config()

       # pad attention mask on the left
       UpperCamelCase__							:       Optional[int]      =   attention_mask.at[(0, 0)].set(0							)

       UpperCamelCase__							:       Tuple      =   False
       UpperCamelCase__							:       str      =   max_length

       for model_class in self.all_generative_model_classes:
           UpperCamelCase__							:       int      =   model_class(__magic_name__							)

           UpperCamelCase__							:       List[Any]      =   model.generate(__magic_name__,	attention_mask=__magic_name__							).sequences
           self.assertEqual(generation_outputs.shape[-1],	__magic_name__							)

           UpperCamelCase__							:       str      =   jit(model.generate							)
           UpperCamelCase__							:       Optional[Any]      =   jit_generate(__magic_name__,	attention_mask=__magic_name__							).sequences

           self.assertListEqual(generation_outputs.tolist(),	jit_generation_outputs.tolist()							)




   def    UpperCamelCase__						(					self							)				->   Tuple:






       """simple docstring"""
       UpperCamelCase__     ,UpperCamelCase__     ,UpperCamelCase__     ,UpperCamelCase__							:       List[str]      =   self._get_input_ids_and_config()

       # pad attention mask on the left
       UpperCamelCase__							:       Any      =   attention_mask.at[(0, 0)].set(0							)

       UpperCamelCase__							:       Any      =   True
       UpperCamelCase__							:       Union[str, Any]      =   max_length

       for model_class in self.all_generative_model_classes:
           UpperCamelCase__							:       Union[str, Any]      =   model_class(__magic_name__							)

           UpperCamelCase__							:       Optional[Any]      =   model.generate(__magic_name__,	attention_mask=__magic_name__							).sequences
           self.assertEqual(generation_outputs.shape[-1],	__magic_name__							)

           UpperCamelCase__							:       Union[str, Any]      =   jit(model.generate							)
           UpperCamelCase__							:       str      =   jit_generate(__magic_name__,	attention_mask=__magic_name__							).sequences

           self.assertListEqual(generation_outputs.tolist(),	jit_generation_outputs.tolist()							)




   def    UpperCamelCase__						(					self							)				->   int:






       """simple docstring"""
       UpperCamelCase__     ,UpperCamelCase__     ,UpperCamelCase__     ,UpperCamelCase__							:       Optional[int]      =   self._get_input_ids_and_config()

       # pad attention mask on the left
       UpperCamelCase__							:       Union[str, Any]      =   attention_mask.at[(0, 0)].set(0							)

       UpperCamelCase__							:       Optional[Any]      =   2
       UpperCamelCase__							:       List[str]      =   max_length

       for model_class in self.all_generative_model_classes:
           UpperCamelCase__							:       int      =   model_class(__magic_name__							)

           UpperCamelCase__							:       Any      =   model.generate(__magic_name__,	attention_mask=__magic_name__							).sequences
           self.assertEqual(generation_outputs.shape[-1],	__magic_name__							)

           UpperCamelCase__							:       Tuple      =   jit(model.generate							)
           UpperCamelCase__							:       int      =   jit_generate(__magic_name__,	attention_mask=__magic_name__							).sequences

           self.assertListEqual(generation_outputs.tolist(),	jit_generation_outputs.tolist()							)






@require_flax
class 					lowercase__ (			unittest.TestCase       ):

   '''simple docstring'''


   def    UpperCamelCase__						(					self							)				->   Tuple:






       """simple docstring"""
       UpperCamelCase__							:       List[str]      =   AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-bert'''							)
       UpperCamelCase__							:       Any      =   FlaxAutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-bert-flax-only'''							)

       UpperCamelCase__							:       int      =   '''Hello world'''
       UpperCamelCase__							:       Tuple      =   tokenizer(__magic_name__,	return_tensors='''np'''							).input_ids

       # typos are quickly detected (the correct argument is `do_sample`)
       with self.assertRaisesRegex(__magic_name__,	'''do_samples'''							):
           model.generate(__magic_name__,	do_samples=__magic_name__							)

       # arbitrary arguments that will not be used anywhere are also not accepted
       with self.assertRaisesRegex(__magic_name__,	'''foo'''							):
           UpperCamelCase__							:       Optional[Any]      =   {'''foo''': '''bar'''}
           model.generate(__magic_name__,	**__magic_name__							)

