






from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
 import PIL

__snake_case				   =    logging.get_logger(__name__)


def     _A  (							SCREAMING_SNAKE_CASE__    :      Union[str, Any]			):
    if isinstance(_UpperCAmelCase	,    (list, tuple)			) and isinstance(videos[0]	,    (list, tuple)			) and is_valid_image(videos[0][0]			):
        return videos

    elif isinstance(_UpperCAmelCase	,    (list, tuple)			) and is_valid_image(videos[0]			):
        return [videos]

    elif is_valid_image(_UpperCAmelCase			):
        return [[videos]]

    raise ValueError(F'''Could not make batched video from {videos}'''			)







class      UpperCAmelCase_		(   __UpperCamelCase					):



      """simple docstring"""
      UpperCamelCase_							:   List[str] 	=['pixel_values']
      def __init__(							self       ,							SCREAMING_SNAKE_CASE_ = True       ,							SCREAMING_SNAKE_CASE_ = None       ,							SCREAMING_SNAKE_CASE_ = PILImageResampling.BILINEAR       ,							SCREAMING_SNAKE_CASE_ = True       ,							SCREAMING_SNAKE_CASE_ = None       ,							SCREAMING_SNAKE_CASE_ = True       ,							SCREAMING_SNAKE_CASE_ = 1 / 255       ,							SCREAMING_SNAKE_CASE_ = True       ,							SCREAMING_SNAKE_CASE_ = True       ,							SCREAMING_SNAKE_CASE_ = None       ,							SCREAMING_SNAKE_CASE_ = None       ,							**SCREAMING_SNAKE_CASE_       ,							)							-> None:
          super().__init__(**SCREAMING_SNAKE_CASE_					)
          UpperCamelCase			:Any      				=  size if size is not None else {'shortest_edge': 256}
          UpperCamelCase			:Optional[Any]      				=  get_size_dict(SCREAMING_SNAKE_CASE_       ,							default_to_square=SCREAMING_SNAKE_CASE_					)
          UpperCamelCase			:str      				=  crop_size if crop_size is not None else {'height': 224, 'width': 224}
          UpperCamelCase			:List[Any]      				=  get_size_dict(SCREAMING_SNAKE_CASE_       ,							param_name='''crop_size'''					)

          UpperCamelCase			:List[Any]      				=  do_resize
          UpperCamelCase			:int      				=  size
          UpperCamelCase			:str      				=  do_center_crop
          UpperCamelCase			:Optional[Any]      				=  crop_size
          UpperCamelCase			:List[Any]      				=  resample
          UpperCamelCase			:Optional[int]      				=  do_rescale
          UpperCamelCase			:Optional[int]      				=  rescale_factor
          UpperCamelCase			:Any      				=  offset
          UpperCamelCase			:List[Any]      				=  do_normalize
          UpperCamelCase			:int      				=  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
          UpperCamelCase			:Any      				=  image_std if image_std is not None else IMAGENET_STANDARD_STD
      def        UpperCAmelCase		(							self       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_ = PILImageResampling.BILINEAR       ,							SCREAMING_SNAKE_CASE_ = None       ,							**SCREAMING_SNAKE_CASE_       ,							)							-> np.ndarray:
          UpperCamelCase			:int      				=  get_size_dict(SCREAMING_SNAKE_CASE_       ,							default_to_square=SCREAMING_SNAKE_CASE_					)
          if "shortest_edge" in size:
              UpperCamelCase			:Tuple      				=  get_resize_output_image_size(SCREAMING_SNAKE_CASE_       ,							size['''shortest_edge''']       ,							default_to_square=SCREAMING_SNAKE_CASE_					)
          elif "height" in size and "width" in size:
              UpperCamelCase			:Tuple      				=  (size['height'], size['width'])
          else:
              raise ValueError(F'''Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'''					)
          return resize(SCREAMING_SNAKE_CASE_       ,							size=SCREAMING_SNAKE_CASE_       ,							resample=SCREAMING_SNAKE_CASE_       ,							data_format=SCREAMING_SNAKE_CASE_       ,							**SCREAMING_SNAKE_CASE_					)
      def        UpperCAmelCase		(							self       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_ = None       ,							**SCREAMING_SNAKE_CASE_       ,							)							-> np.ndarray:
          UpperCamelCase			:str      				=  get_size_dict(SCREAMING_SNAKE_CASE_					)
          if "height" not in size or "width" not in size:
              raise ValueError(F'''Size must have \'height\' and \'width\' as keys. Got {size.keys()}'''					)
          return center_crop(SCREAMING_SNAKE_CASE_       ,							size=(size['''height'''], size['''width'''])       ,							data_format=SCREAMING_SNAKE_CASE_       ,							**SCREAMING_SNAKE_CASE_					)
      def        UpperCAmelCase		(							self       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_ = True       ,							SCREAMING_SNAKE_CASE_ = None       ,							**SCREAMING_SNAKE_CASE_       ,							)							-> Any:
          UpperCamelCase			:int      				=  image.astype(np.floataa					)
          if offset:
              UpperCamelCase			:List[Any]      				=  image - (scale / 2)
          return rescale(SCREAMING_SNAKE_CASE_       ,							scale=SCREAMING_SNAKE_CASE_       ,							data_format=SCREAMING_SNAKE_CASE_       ,							**SCREAMING_SNAKE_CASE_					)
      def        UpperCAmelCase		(							self       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_ = None       ,							**SCREAMING_SNAKE_CASE_       ,							)							-> np.ndarray:
          return normalize(SCREAMING_SNAKE_CASE_       ,							mean=SCREAMING_SNAKE_CASE_       ,							std=SCREAMING_SNAKE_CASE_       ,							data_format=SCREAMING_SNAKE_CASE_       ,							**SCREAMING_SNAKE_CASE_					)
      def        UpperCAmelCase		(							self       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_ = None       ,							SCREAMING_SNAKE_CASE_ = None       ,							SCREAMING_SNAKE_CASE_ = None       ,							SCREAMING_SNAKE_CASE_ = None       ,							SCREAMING_SNAKE_CASE_ = None       ,							SCREAMING_SNAKE_CASE_ = None       ,							SCREAMING_SNAKE_CASE_ = None       ,							SCREAMING_SNAKE_CASE_ = None       ,							SCREAMING_SNAKE_CASE_ = None       ,							SCREAMING_SNAKE_CASE_ = None       ,							SCREAMING_SNAKE_CASE_ = None       ,							SCREAMING_SNAKE_CASE_ = ChannelDimension.FIRST       ,							)							-> np.ndarray:
          if do_resize and size is None or resample is None:
              raise ValueError('''Size and resample must be specified if do_resize is True.'''					)

          if do_center_crop and crop_size is None:
              raise ValueError('''Crop size must be specified if do_center_crop is True.'''					)

          if do_rescale and rescale_factor is None:
              raise ValueError('''Rescale factor must be specified if do_rescale is True.'''					)

          if do_normalize and (image_mean is None or image_std is None):
              raise ValueError('''Image mean and std must be specified if do_normalize is True.'''					)

          if offset and not do_rescale:
              raise ValueError('''For offset, do_rescale must also be set to True.'''					)

          # All transformations expect numpy arrays.
          UpperCamelCase			:List[Any]      				=  to_numpy_array(SCREAMING_SNAKE_CASE_					)

          if do_resize:
              UpperCamelCase			:Any      				=  self.resize(image=SCREAMING_SNAKE_CASE_       ,							size=SCREAMING_SNAKE_CASE_       ,							resample=SCREAMING_SNAKE_CASE_					)

          if do_center_crop:
              UpperCamelCase			:Dict      				=  self.center_crop(SCREAMING_SNAKE_CASE_       ,							size=SCREAMING_SNAKE_CASE_					)

          if do_rescale:
              UpperCamelCase			:Union[str, Any]      				=  self.rescale(image=SCREAMING_SNAKE_CASE_       ,							scale=SCREAMING_SNAKE_CASE_       ,							offset=SCREAMING_SNAKE_CASE_					)

          if do_normalize:
              UpperCamelCase			:str      				=  self.normalize(image=SCREAMING_SNAKE_CASE_       ,							mean=SCREAMING_SNAKE_CASE_       ,							std=SCREAMING_SNAKE_CASE_					)

          UpperCamelCase			:Optional[Any]      				=  to_channel_dimension_format(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_					)
          return image




      def        UpperCAmelCase		(							self       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_ = None       ,							SCREAMING_SNAKE_CASE_ = None       ,							SCREAMING_SNAKE_CASE_ = None       ,							SCREAMING_SNAKE_CASE_ = None       ,							SCREAMING_SNAKE_CASE_ = None       ,							SCREAMING_SNAKE_CASE_ = None       ,							SCREAMING_SNAKE_CASE_ = None       ,							SCREAMING_SNAKE_CASE_ = None       ,							SCREAMING_SNAKE_CASE_ = None       ,							SCREAMING_SNAKE_CASE_ = None       ,							SCREAMING_SNAKE_CASE_ = None       ,							SCREAMING_SNAKE_CASE_ = None       ,							SCREAMING_SNAKE_CASE_ = ChannelDimension.FIRST       ,							**SCREAMING_SNAKE_CASE_       ,							)							-> PIL.Image.Image:
          UpperCamelCase			:List[Any]      				=  do_resize if do_resize is not None else self.do_resize
          UpperCamelCase			:Any      				=  resample if resample is not None else self.resample
          UpperCamelCase			:str      				=  do_center_crop if do_center_crop is not None else self.do_center_crop
          UpperCamelCase			:Optional[int]      				=  do_rescale if do_rescale is not None else self.do_rescale
          UpperCamelCase			:Optional[int]      				=  rescale_factor if rescale_factor is not None else self.rescale_factor
          UpperCamelCase			:int      				=  offset if offset is not None else self.offset
          UpperCamelCase			:List[str]      				=  do_normalize if do_normalize is not None else self.do_normalize
          UpperCamelCase			:Optional[Any]      				=  image_mean if image_mean is not None else self.image_mean
          UpperCamelCase			:Dict      				=  image_std if image_std is not None else self.image_std

          UpperCamelCase			:int      				=  size if size is not None else self.size
          UpperCamelCase			:Optional[Any]      				=  get_size_dict(SCREAMING_SNAKE_CASE_       ,							default_to_square=SCREAMING_SNAKE_CASE_					)
          UpperCamelCase			:Union[str, Any]      				=  crop_size if crop_size is not None else self.crop_size
          UpperCamelCase			:List[str]      				=  get_size_dict(SCREAMING_SNAKE_CASE_       ,							param_name='''crop_size'''					)

          if not valid_images(SCREAMING_SNAKE_CASE_					):
              raise ValueError(
                  '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
                  '''torch.Tensor, tf.Tensor or jax.ndarray.'''					)

          UpperCamelCase			:List[Any]      				=  make_batched(SCREAMING_SNAKE_CASE_					)

          UpperCamelCase			:Dict      				=  [
              [
                  self._preprocess_image(
                      image=SCREAMING_SNAKE_CASE_       ,							do_resize=SCREAMING_SNAKE_CASE_       ,							size=SCREAMING_SNAKE_CASE_       ,							resample=SCREAMING_SNAKE_CASE_       ,							do_center_crop=SCREAMING_SNAKE_CASE_       ,							crop_size=SCREAMING_SNAKE_CASE_       ,							do_rescale=SCREAMING_SNAKE_CASE_       ,							rescale_factor=SCREAMING_SNAKE_CASE_       ,							offset=SCREAMING_SNAKE_CASE_       ,							do_normalize=SCREAMING_SNAKE_CASE_       ,							image_mean=SCREAMING_SNAKE_CASE_       ,							image_std=SCREAMING_SNAKE_CASE_       ,							data_format=SCREAMING_SNAKE_CASE_       ,							)
                  for img in video
              ]
              for video in videos
          ]

          UpperCamelCase			:int      				=  {'pixel_values': videos}
          return BatchFeature(data=SCREAMING_SNAKE_CASE_       ,							tensor_type=SCREAMING_SNAKE_CASE_					)












import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
    import jax
    import jax.numpy as jnp

    from transformers.modeling_flax_pytorch_utils import (
        convert_pytorch_state_dict_to_flax,
        load_flax_weights_in_pytorch_model,
    )
    from transformers.models.gptj.modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel

if is_torch_available():
    import torch

class   lowerCAmelCase				:
 def __init__( self    : List[str]     ,	UpperCAmelCase    : List[Any]     ,	UpperCAmelCase    : str=14     ,	UpperCAmelCase    : Any=7     ,	UpperCAmelCase    : Dict=True     ,	UpperCAmelCase    : List[str]=True     ,	UpperCAmelCase    : Any=False     ,	UpperCAmelCase    : Tuple=True     ,	UpperCAmelCase    : int=99     ,	UpperCAmelCase    : str=32     ,	UpperCAmelCase    : int=4     ,	UpperCAmelCase    : Optional[Any]=4     ,	UpperCAmelCase    : Any=4     ,	UpperCAmelCase    : Optional[int]=37     ,	UpperCAmelCase    : Union[str, Any]="gelu"     ,	UpperCAmelCase    : List[Any]=0.1     ,	UpperCAmelCase    : Dict=0.1     ,	UpperCAmelCase    : Optional[int]=512     ,	UpperCAmelCase    : List[Any]=0.0_2     ,	)     ->      List[Any]:
     lowerCamelCase__       : Tuple   					=						parent
     lowerCamelCase__       : Tuple   					=						batch_size
     lowerCamelCase__       : Optional[Any]   					=						seq_length
     lowerCamelCase__       : str   					=						is_training
     lowerCamelCase__       : Any   					=						use_input_mask
     lowerCamelCase__       : Optional[Any]   					=						use_token_type_ids
     lowerCamelCase__       : Optional[Any]   					=						use_labels
     lowerCamelCase__       : Optional[Any]   					=						vocab_size
     lowerCamelCase__       : Optional[int]   					=						hidden_size
     lowerCamelCase__       : Optional[Any]   					=						rotary_dim
     lowerCamelCase__       : Optional[Any]   					=						num_hidden_layers
     lowerCamelCase__       : Optional[Any]   					=						num_attention_heads
     lowerCamelCase__       : Tuple   					=						intermediate_size
     lowerCamelCase__       : Union[str, Any]   					=						hidden_act
     lowerCamelCase__       : List[Any]   					=						hidden_dropout_prob
     lowerCamelCase__       : int   					=						attention_probs_dropout_prob
     lowerCamelCase__       : Dict   					=						max_position_embeddings
     lowerCamelCase__       : Tuple   					=						initializer_range
     lowerCamelCase__       : Optional[int]   					=						None
     lowerCamelCase__       : int   					=						vocab_size - 1
     lowerCamelCase__       : str   					=						vocab_size - 1
     lowerCamelCase__       : str   					=						vocab_size - 1
 def 	A_      ( self    : str      )     ->      int:
     lowerCamelCase__       : Optional[int]   					=						ids_tensor([self.batch_size, self.seq_length]     ,	self.vocab_size      )

     lowerCamelCase__       : Optional[Any]   					=						None
     if self.use_input_mask:
         lowerCamelCase__       : List[str]   					=						random_attention_mask([self.batch_size, self.seq_length]      )

     lowerCamelCase__       : Tuple   					=						GPTJConfig(
         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=UpperCAmelCase     ,	bos_token_id=self.bos_token_id     ,	eos_token_id=self.eos_token_id     ,	pad_token_id=self.pad_token_id     ,	rotary_dim=self.rotary_dim     ,	)

     return (config, input_ids, input_mask)
 def 	A_      ( self    : Optional[Any]      )     ->      Union[str, Any]:
     lowerCamelCase__       : Tuple   					=						self.prepare_config_and_inputs()
     lowerCamelCase__					,				lowerCamelCase__					,				lowerCamelCase__       : List[Any]   					=						config_and_inputs
     lowerCamelCase__       : List[Any]   					=						{'input_ids': input_ids, 'attention_mask': attention_mask}
     return config, inputs_dict
 def 	A_      ( self    : int     ,	UpperCAmelCase    : Optional[Any]     ,	UpperCAmelCase    : Any     ,	UpperCAmelCase    : List[str]     ,	UpperCAmelCase    : Optional[Any]      )     ->      Tuple:
     lowerCamelCase__       : Tuple   					=						20
     lowerCamelCase__       : Dict   					=						model_class_name(UpperCAmelCase      )

     lowerCamelCase__       : Dict   					=						model.init_cache(input_ids.shape[0]     ,	UpperCAmelCase      )
     lowerCamelCase__       : int   					=						jnp.ones((input_ids.shape[0], max_decoder_length)     ,	dtype='i4'      )

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

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

     lowerCamelCase__       : List[str]   					=						model(UpperCAmelCase      )

     lowerCamelCase__       : Optional[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}"""      )







 def 	A_      ( self    : Union[str, Any]     ,	UpperCAmelCase    : Any     ,	UpperCAmelCase    : Any     ,	UpperCAmelCase    : List[Any]     ,	UpperCAmelCase    : List[str]      )     ->      Optional[Any]:
     lowerCamelCase__       : Any   					=						20
     lowerCamelCase__       : Any   					=						model_class_name(UpperCAmelCase      )

     lowerCamelCase__       : List[str]   					=						jnp.concatenate(
         [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])      )]     ,	axis=-1     ,	)

     lowerCamelCase__       : str   					=						model.init_cache(input_ids.shape[0]     ,	UpperCAmelCase      )
     lowerCamelCase__       : Dict   					=						jnp.broadcast_to(
         jnp.arange(input_ids.shape[-1] - 1      )[None, :]     ,	(input_ids.shape[0], input_ids.shape[-1] - 1)      )

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

     lowerCamelCase__       : List[Any]   					=						model(UpperCAmelCase     ,	attention_mask=UpperCAmelCase      )

     lowerCamelCase__       : Dict   					=						np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])      )      )
     self.parent.assertTrue(diff < 1e-3     ,	msg=F"""Max diff is {diff}"""      )


@require_flax
class   lowerCAmelCase				(   __UpperCamelCase,    __UpperCamelCase,    unittest.TestCase     ):
 UpperCAmelCase__						=						(FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
 UpperCAmelCase__						=						(FlaxGPTJForCausalLM,) if is_flax_available() else ()
 def 	A_      ( self    : Optional[Any]      )     ->      Tuple:
     lowerCamelCase__       : Optional[Any]   					=						FlaxGPTJModelTester(self      )
 def 	A_      ( self    : Tuple      )     ->      Union[str, Any]:
     for model_class_name in self.all_model_classes:
         lowerCamelCase__					,				lowerCamelCase__					,				lowerCamelCase__       : Optional[int]   					=						self.model_tester.prepare_config_and_inputs()
         self.model_tester.check_use_cache_forward(UpperCAmelCase     ,	UpperCAmelCase     ,	UpperCAmelCase     ,	UpperCAmelCase      )
 def 	A_      ( self    : Union[str, Any]      )     ->      Tuple:
     for model_class_name in self.all_model_classes:
         lowerCamelCase__					,				lowerCamelCase__					,				lowerCamelCase__       : str   					=						self.model_tester.prepare_config_and_inputs()
         self.model_tester.check_use_cache_forward_with_attn_mask(
             UpperCAmelCase     ,	UpperCAmelCase     ,	UpperCAmelCase     ,	UpperCAmelCase      )
 @tooslow
 def 	A_      ( self    : Dict      )     ->      Tuple:
     lowerCamelCase__       : str   					=						GPTaTokenizer.from_pretrained('gpt2'     ,	pad_token='<|endoftext|>'     ,	padding_side='left'      )
     lowerCamelCase__       : Dict   					=						tokenizer(['Hello this is a long string', 'Hey']     ,	return_tensors='np'     ,	padding=UpperCAmelCase     ,	truncation=UpperCAmelCase      )

     lowerCamelCase__       : str   					=						FlaxGPTJForCausalLM.from_pretrained('EleutherAI/gpt-j-6B'      )
     lowerCamelCase__       : Optional[int]   					=						False
     lowerCamelCase__       : Optional[int]   					=						model.config.eos_token_id

     lowerCamelCase__       : Union[str, Any]   					=						jax.jit(model.generate      )

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

     lowerCamelCase__       : Optional[int]   					=						tokenizer.batch_decode(UpperCAmelCase     ,	skip_special_tokens=UpperCAmelCase      )

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

     self.assertListEqual(UpperCAmelCase     ,	UpperCAmelCase      )
 @is_pt_flax_cross_test
 def 	A_      ( self    : List[str]      )     ->      Union[str, Any]:
     lowerCamelCase__					,				lowerCamelCase__       : 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
             lowerCamelCase__       : Dict   					=						self._prepare_for_class(UpperCAmelCase     ,	UpperCAmelCase      )
             lowerCamelCase__       : Optional[int]   					=						{k: torch.tensor(v.tolist()      ) for k, v in prepared_inputs_dict.items()}

             # load corresponding PyTorch class
             lowerCamelCase__       : Any   					=						model_class.__name__[4:]  # Skip the "Flax" at the beginning
             lowerCamelCase__       : Union[str, Any]   					=						getattr(UpperCAmelCase     ,	UpperCAmelCase      )

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

             lowerCamelCase__       : str   					=						convert_pytorch_state_dict_to_flax(pt_model.state_dict()     ,	UpperCAmelCase      )
             lowerCamelCase__       : Tuple   					=						fx_state

             with torch.no_grad():
                 lowerCamelCase__       : List[str]   					=						pt_model(**UpperCAmelCase      ).to_tuple()

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

             with tempfile.TemporaryDirectory() as tmpdirname:
                 pt_model.save_pretrained(UpperCAmelCase      )
                 lowerCamelCase__       : Tuple   					=						model_class.from_pretrained(UpperCAmelCase     ,	from_pt=UpperCAmelCase      )

             lowerCamelCase__       : int   					=						fx_model_loaded(**UpperCAmelCase      ).to_tuple()
             self.assertEqual(
                 len(UpperCAmelCase      )     ,	len(UpperCAmelCase      )     ,	'Output lengths differ between Flax and PyTorch'      )
             for fx_output_loaded, pt_output in zip(UpperCAmelCase     ,	UpperCAmelCase      ):
                 self.assert_almost_equals(fx_output_loaded[:, -1]     ,	pt_output[:, -1].numpy()     ,	4e-2      )
 @is_pt_flax_cross_test
 def 	A_      ( self    : Union[str, Any]      )     ->      int:
     lowerCamelCase__					,				lowerCamelCase__       : Dict   					=						self.model_tester.prepare_config_and_inputs_for_common()
     for model_class in self.all_model_classes:
         with self.subTest(model_class.__name__      ):
             # prepare inputs
             lowerCamelCase__       : Tuple   					=						self._prepare_for_class(UpperCAmelCase     ,	UpperCAmelCase      )
             lowerCamelCase__       : Tuple   					=						{k: torch.tensor(v.tolist()      ) for k, v in prepared_inputs_dict.items()}

             # load corresponding PyTorch class
             lowerCamelCase__       : Union[str, Any]   					=						model_class.__name__[4:]  # Skip the "Flax" at the beginning
             lowerCamelCase__       : int   					=						getattr(UpperCAmelCase     ,	UpperCAmelCase      )

             lowerCamelCase__       : List[Any]   					=						pt_model_class(UpperCAmelCase      ).eval()
             lowerCamelCase__       : str   					=						model_class(UpperCAmelCase     ,	dtype=jnp.floataa      )

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

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

             with torch.no_grad():
                 lowerCamelCase__       : Any   					=						pt_model(**UpperCAmelCase      ).to_tuple()

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

             with tempfile.TemporaryDirectory() as tmpdirname:
                 fx_model.save_pretrained(UpperCAmelCase      )
                 lowerCamelCase__       : Tuple   					=						pt_model_class.from_pretrained(UpperCAmelCase     ,	from_flax=UpperCAmelCase      )

             with torch.no_grad():
                 lowerCamelCase__       : Optional[int]   					=						pt_model_loaded(**UpperCAmelCase      ).to_tuple()

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







 @tooslow
 def 	A_      ( self    : List[str]      )     ->      List[Any]:
     for model_class_name in self.all_model_classes:
         lowerCamelCase__       : Union[str, Any]   					=						model_class_name.from_pretrained('EleutherAI/gpt-j-6B'      )
         lowerCamelCase__       : Any   					=						model(np.ones((1, 1)      )      )
         self.assertIsNotNone(UpperCAmelCase      )





