




from typing import Dict, List, Optional, Union

import numpy as np

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 TensorType, is_vision_available, logging


if is_vision_available():
  import PIL


_lowerCamelCase     :		List[Any]								=       logging.get_logger(__name__)







def SCREAMING_SNAKE_CASE    (	lowercase_						)       ->	List[List[ImageInput]]:



       """simple docstring"""

       if isinstance(lowercase_			,    (list, tuple)						) and isinstance(videos[0]			,    (list, tuple)						) and is_valid_image(videos[0][0]						):
              return videos

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

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

       raise ValueError(f"""Could not make batched video from {videos}"""						)


class     UpperCamelCase_   (			UpperCAmelCase__					):

    '''simple docstring'''






    UpperCAmelCase__							  =				['''pixel_values''']


    def __init__(      self				:     List[str] ,			UpperCAmelCase__				:     bool = True ,			UpperCAmelCase__				:     Dict[str, int] = None ,			UpperCAmelCase__				:     PILImageResampling = PILImageResampling.BILINEAR ,			UpperCAmelCase__				:     bool = True ,			UpperCAmelCase__				:     Dict[str, int] = None ,			UpperCAmelCase__				:     bool = True ,			UpperCAmelCase__				:     Union[int, float] = 1 / 255 ,			UpperCAmelCase__				:     bool = True ,			UpperCAmelCase__				:     Optional[Union[float, List[float]]] = None ,			UpperCAmelCase__				:     Optional[Union[float, List[float]]] = None ,			**UpperCAmelCase__				:     List[str] ,			)						->None:





           '''simple docstring'''

           super().__init__(**UpperCAmelCase__)
           A__							=   size if size is not None else {'''shortest_edge''': 224}
           A__							=   get_size_dict(UpperCAmelCase__ ,			default_to_square=UpperCAmelCase__)
           A__							=   crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
           A__							=   get_size_dict(UpperCAmelCase__ ,			param_name='''crop_size''')

           A__							=   do_resize
           A__							=   size
           A__							=   do_center_crop
           A__							=   crop_size
           A__							=   resample
           A__							=   do_rescale
           A__							=   rescale_factor
           A__							=   do_normalize
           A__							=   image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
           A__							=   image_std if image_std is not None else IMAGENET_STANDARD_STD


    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[Any] ,			UpperCAmelCase__				:     np.ndarray ,			UpperCAmelCase__				:     Dict[str, int] ,			UpperCAmelCase__				:     PILImageResampling = PILImageResampling.BILINEAR ,			UpperCAmelCase__				:     Optional[Union[str, ChannelDimension]] = None ,			**UpperCAmelCase__				:     Union[str, Any] ,			)						->np.ndarray:





           '''simple docstring'''

           A__							=   get_size_dict(UpperCAmelCase__ ,			default_to_square=UpperCAmelCase__)
           if "shortest_edge" in size:
                  A__							=   get_resize_output_image_size(UpperCAmelCase__ ,			size['''shortest_edge'''] ,			default_to_square=UpperCAmelCase__)
           elif "height" in size and "width" in size:
                  A__							=   (size['''height'''], size['''width'''])
           else:
                  raise ValueError(f"""Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}""")
           return resize(UpperCAmelCase__ ,			size=UpperCAmelCase__ ,			resample=UpperCAmelCase__ ,			data_format=UpperCAmelCase__ ,			**UpperCAmelCase__)


    def 	SCREAMING_SNAKE_CASE							(      self				:     str ,			UpperCAmelCase__				:     np.ndarray ,			UpperCAmelCase__				:     Dict[str, int] ,			UpperCAmelCase__				:     Optional[Union[str, ChannelDimension]] = None ,			**UpperCAmelCase__				:     Optional[Any] ,			)						->np.ndarray:





           '''simple docstring'''

           A__							=   get_size_dict(UpperCAmelCase__)
           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(UpperCAmelCase__ ,			size=(size['''height'''], size['''width''']) ,			data_format=UpperCAmelCase__ ,			**UpperCAmelCase__)


    def 	SCREAMING_SNAKE_CASE							(      self				:     List[str] ,			UpperCAmelCase__				:     np.ndarray ,			UpperCAmelCase__				:     Union[int, float] ,			UpperCAmelCase__				:     Optional[Union[str, ChannelDimension]] = None ,			**UpperCAmelCase__				:     Optional[Any] ,			)						->List[str]:





           '''simple docstring'''

           return rescale(UpperCAmelCase__ ,			scale=UpperCAmelCase__ ,			data_format=UpperCAmelCase__ ,			**UpperCAmelCase__)


    def 	SCREAMING_SNAKE_CASE							(      self				:     Union[str, Any] ,			UpperCAmelCase__				:     np.ndarray ,			UpperCAmelCase__				:     Union[float, List[float]] ,			UpperCAmelCase__				:     Union[float, List[float]] ,			UpperCAmelCase__				:     Optional[Union[str, ChannelDimension]] = None ,			**UpperCAmelCase__				:     int ,			)						->np.ndarray:





           '''simple docstring'''

           return normalize(UpperCAmelCase__ ,			mean=UpperCAmelCase__ ,			std=UpperCAmelCase__ ,			data_format=UpperCAmelCase__ ,			**UpperCAmelCase__)


    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[int] ,			UpperCAmelCase__				:     ImageInput ,			UpperCAmelCase__				:     bool = None ,			UpperCAmelCase__				:     Dict[str, int] = None ,			UpperCAmelCase__				:     PILImageResampling = None ,			UpperCAmelCase__				:     bool = None ,			UpperCAmelCase__				:     Dict[str, int] = None ,			UpperCAmelCase__				:     bool = None ,			UpperCAmelCase__				:     float = None ,			UpperCAmelCase__				:     bool = None ,			UpperCAmelCase__				:     Optional[Union[float, List[float]]] = None ,			UpperCAmelCase__				:     Optional[Union[float, List[float]]] = None ,			UpperCAmelCase__				:     Optional[ChannelDimension] = ChannelDimension.FIRST ,			)						->np.ndarray:





           '''simple docstring'''

           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.''')

           # All transformations expect numpy arrays.
           A__							=   to_numpy_array(UpperCAmelCase__)

           if do_resize:
                  A__							=   self.resize(image=UpperCAmelCase__ ,			size=UpperCAmelCase__ ,			resample=UpperCAmelCase__)

           if do_center_crop:
                  A__							=   self.center_crop(UpperCAmelCase__ ,			size=UpperCAmelCase__)

           if do_rescale:
                  A__							=   self.rescale(image=UpperCAmelCase__ ,			scale=UpperCAmelCase__)

           if do_normalize:
                  A__							=   self.normalize(image=UpperCAmelCase__ ,			mean=UpperCAmelCase__ ,			std=UpperCAmelCase__)

           A__							=   to_channel_dimension_format(UpperCAmelCase__ ,			UpperCAmelCase__)
           return image





    def 	SCREAMING_SNAKE_CASE							(      self				:     Tuple ,			UpperCAmelCase__				:     ImageInput ,			UpperCAmelCase__				:     bool = None ,			UpperCAmelCase__				:     Dict[str, int] = None ,			UpperCAmelCase__				:     PILImageResampling = None ,			UpperCAmelCase__				:     bool = None ,			UpperCAmelCase__				:     Dict[str, int] = None ,			UpperCAmelCase__				:     bool = None ,			UpperCAmelCase__				:     float = None ,			UpperCAmelCase__				:     bool = None ,			UpperCAmelCase__				:     Optional[Union[float, List[float]]] = None ,			UpperCAmelCase__				:     Optional[Union[float, List[float]]] = None ,			UpperCAmelCase__				:     Optional[Union[str, TensorType]] = None ,			UpperCAmelCase__				:     ChannelDimension = ChannelDimension.FIRST ,			**UpperCAmelCase__				:     Dict ,			)						->PIL.Image.Image:





           '''simple docstring'''

           A__							=   do_resize if do_resize is not None else self.do_resize
           A__							=   resample if resample is not None else self.resample
           A__							=   do_center_crop if do_center_crop is not None else self.do_center_crop
           A__							=   do_rescale if do_rescale is not None else self.do_rescale
           A__							=   rescale_factor if rescale_factor is not None else self.rescale_factor
           A__							=   do_normalize if do_normalize is not None else self.do_normalize
           A__							=   image_mean if image_mean is not None else self.image_mean
           A__							=   image_std if image_std is not None else self.image_std

           A__							=   size if size is not None else self.size
           A__							=   get_size_dict(UpperCAmelCase__ ,			default_to_square=UpperCAmelCase__)
           A__							=   crop_size if crop_size is not None else self.crop_size
           A__							=   get_size_dict(UpperCAmelCase__ ,			param_name='''crop_size''')

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

           A__							=   make_batched(UpperCAmelCase__)

           A__							=   [
               [
                   self._preprocess_image(
                       image=UpperCAmelCase__ ,			do_resize=UpperCAmelCase__ ,			size=UpperCAmelCase__ ,			resample=UpperCAmelCase__ ,			do_center_crop=UpperCAmelCase__ ,			crop_size=UpperCAmelCase__ ,			do_rescale=UpperCAmelCase__ ,			rescale_factor=UpperCAmelCase__ ,			do_normalize=UpperCAmelCase__ ,			image_mean=UpperCAmelCase__ ,			image_std=UpperCAmelCase__ ,			data_format=UpperCAmelCase__ ,			)
                   for img in video
               ]
               for video in videos
           ]

           A__							=   {'''pixel_values''': videos}
           return BatchFeature(data=UpperCAmelCase__ ,			tensor_type=UpperCAmelCase__)








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     UpperCamelCase_   :

    '''simple docstring'''






    def __init__(      self				:     str ,			UpperCAmelCase__				:     List[Any] ,			UpperCAmelCase__				:     Any=14 ,			UpperCAmelCase__				:     Any=7 ,			UpperCAmelCase__				:     Dict=True ,			UpperCAmelCase__				:     int=True ,			UpperCAmelCase__				:     Tuple=False ,			UpperCAmelCase__				:     Optional[int]=True ,			UpperCAmelCase__				:     Tuple=99 ,			UpperCAmelCase__				:     Optional[Any]=32 ,			UpperCAmelCase__				:     Tuple=4 ,			UpperCAmelCase__				:     Any=4 ,			UpperCAmelCase__				:     int=4 ,			UpperCAmelCase__				:     Optional[Any]=37 ,			UpperCAmelCase__				:     Union[str, Any]="gelu" ,			UpperCAmelCase__				:     Optional[int]=0.1 ,			UpperCAmelCase__				:     int=0.1 ,			UpperCAmelCase__				:     Optional[Any]=512 ,			UpperCAmelCase__				:     Union[str, Any]=0.02 ,			)						->Any:





           '''simple docstring'''

           A__							=   parent
           A__							=   batch_size
           A__							=   seq_length
           A__							=   is_training
           A__							=   use_input_mask
           A__							=   use_token_type_ids
           A__							=   use_labels
           A__							=   vocab_size
           A__							=   hidden_size
           A__							=   rotary_dim
           A__							=   num_hidden_layers
           A__							=   num_attention_heads
           A__							=   intermediate_size
           A__							=   hidden_act
           A__							=   hidden_dropout_prob
           A__							=   attention_probs_dropout_prob
           A__							=   max_position_embeddings
           A__							=   initializer_range
           A__							=   None
           A__							=   vocab_size - 1
           A__							=   vocab_size - 1
           A__							=   vocab_size - 1


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





           '''simple docstring'''

           A__							=   ids_tensor([self.batch_size, self.seq_length] ,			self.vocab_size)

           A__							=   None
           if self.use_input_mask:
                  A__							=   random_attention_mask([self.batch_size, self.seq_length])

           A__							=   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 	SCREAMING_SNAKE_CASE							(      self				:     Any)						->Optional[int]:





           '''simple docstring'''

           A__							=   self.prepare_config_and_inputs()
           A__			,							A__			,							A__							=   config_and_inputs
           A__							=   {'''input_ids''': input_ids, '''attention_mask''': attention_mask}
           return config, inputs_dict


    def 	SCREAMING_SNAKE_CASE							(      self				:     Any ,			UpperCAmelCase__				:     int ,			UpperCAmelCase__				:     Tuple ,			UpperCAmelCase__				:     Union[str, Any] ,			UpperCAmelCase__				:     List[Any])						->Optional[int]:





           '''simple docstring'''

           A__							=   20
           A__							=   model_class_name(UpperCAmelCase__)

           A__							=   model.init_cache(input_ids.shape[0] ,			UpperCAmelCase__)
           A__							=   jnp.ones((input_ids.shape[0], max_decoder_length) ,			dtype='''i4''')

           A__							=   jnp.broadcast_to(
               jnp.arange(input_ids.shape[-1] - 1)[None, :] ,			(input_ids.shape[0], input_ids.shape[-1] - 1))
           A__							=   model(
               input_ids[:, :-1] ,			attention_mask=UpperCAmelCase__ ,			past_key_values=UpperCAmelCase__ ,			position_ids=UpperCAmelCase__ ,			)

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

           A__							=   model(UpperCAmelCase__)

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





    def 	SCREAMING_SNAKE_CASE							(      self				:     Tuple ,			UpperCAmelCase__				:     Optional[Any] ,			UpperCAmelCase__				:     Dict ,			UpperCAmelCase__				:     List[Any] ,			UpperCAmelCase__				:     int)						->Any:





           '''simple docstring'''

           A__							=   20
           A__							=   model_class_name(UpperCAmelCase__)

           A__							=   jnp.concatenate(
               [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1]))] ,			axis=-1 ,			)

           A__							=   model.init_cache(input_ids.shape[0] ,			UpperCAmelCase__)
           A__							=   jnp.broadcast_to(
               jnp.arange(input_ids.shape[-1] - 1)[None, :] ,			(input_ids.shape[0], input_ids.shape[-1] - 1))

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

           A__							=   model(UpperCAmelCase__ ,			attention_mask=UpperCAmelCase__)

           A__							=   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     UpperCamelCase_   (			UpperCAmelCase__		, UpperCAmelCase__		, unittest.TestCase					):

    '''simple docstring'''






    UpperCAmelCase__							  =				(FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
    UpperCAmelCase__							  =				(FlaxGPTJForCausalLM,) if is_flax_available() else ()


    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[Any])						->List[Any]:





           '''simple docstring'''

           A__							=   FlaxGPTJModelTester(self)


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





           '''simple docstring'''

           for model_class_name in self.all_model_classes:
                  A__			,							A__			,							A__							=   self.model_tester.prepare_config_and_inputs()
                  self.model_tester.check_use_cache_forward(UpperCAmelCase__ ,			UpperCAmelCase__ ,			UpperCAmelCase__ ,			UpperCAmelCase__)


    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[int])						->Any:





           '''simple docstring'''

           for model_class_name in self.all_model_classes:
                  A__			,							A__			,							A__							=   self.model_tester.prepare_config_and_inputs()
                  self.model_tester.check_use_cache_forward_with_attn_mask(
                      UpperCAmelCase__ ,			UpperCAmelCase__ ,			UpperCAmelCase__ ,			UpperCAmelCase__)


    @tooslow
    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[Any])						->Dict:





           '''simple docstring'''

           A__							=   GPTaTokenizer.from_pretrained('''gpt2''' ,			pad_token='''<|endoftext|>''' ,			padding_side='''left''')
           A__							=   tokenizer(['''Hello this is a long string''', '''Hey'''] ,			return_tensors='''np''' ,			padding=UpperCAmelCase__ ,			truncation=UpperCAmelCase__)

           A__							=   FlaxGPTJForCausalLM.from_pretrained('''EleutherAI/gpt-j-6B''')
           A__							=   False
           A__							=   model.config.eos_token_id

           A__							=   jax.jit(model.generate)

           A__							=   jit_generate(
               inputs['''input_ids'''] ,			attention_mask=inputs['''attention_mask'''] ,			pad_token_id=tokenizer.pad_token_id).sequences

           A__							=   tokenizer.batch_decode(UpperCAmelCase__ ,			skip_special_tokens=UpperCAmelCase__)

           A__							=   [
               '''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 	SCREAMING_SNAKE_CASE							(      self				:     Optional[int])						->int:





           '''simple docstring'''

           A__			,							A__							=   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
                         A__							=   self._prepare_for_class(UpperCAmelCase__ ,			UpperCAmelCase__)
                         A__							=   {k: torch.tensor(v.tolist()) for k, v in prepared_inputs_dict.items()}

                         # load corresponding PyTorch class
                         A__							=   model_class.__name__[4:]  # Skip the "Flax" at the beginning
                         A__							=   getattr(UpperCAmelCase__ ,			UpperCAmelCase__)

                         A__			,							A__							=   pt_inputs['''input_ids'''].shape
                         A__							=   np.random.randint(0 ,			seq_length - 1 ,			size=(batch_size,))
                         for batch_idx, start_index in enumerate(UpperCAmelCase__):
                                A__							=   0
                                A__							=   1
                                A__							=   0
                                A__							=   1
                         A__							=   pt_model_class(UpperCAmelCase__).eval()
                         A__							=   model_class(UpperCAmelCase__ ,			dtype=jnp.floataa)

                         A__							=   convert_pytorch_state_dict_to_flax(pt_model.state_dict() ,			UpperCAmelCase__)
                         A__							=   fx_state

                         with torch.no_grad():
                                A__							=   pt_model(**UpperCAmelCase__).to_tuple()

                         A__							=   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__)
                                A__							=   model_class.from_pretrained(UpperCAmelCase__ ,			from_pt=UpperCAmelCase__)

                         A__							=   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 	SCREAMING_SNAKE_CASE							(      self				:     Any)						->Optional[int]:





           '''simple docstring'''

           A__			,							A__							=   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
                         A__							=   self._prepare_for_class(UpperCAmelCase__ ,			UpperCAmelCase__)
                         A__							=   {k: torch.tensor(v.tolist()) for k, v in prepared_inputs_dict.items()}

                         # load corresponding PyTorch class
                         A__							=   model_class.__name__[4:]  # Skip the "Flax" at the beginning
                         A__							=   getattr(UpperCAmelCase__ ,			UpperCAmelCase__)

                         A__							=   pt_model_class(UpperCAmelCase__).eval()
                         A__							=   model_class(UpperCAmelCase__ ,			dtype=jnp.floataa)

                         A__							=   load_flax_weights_in_pytorch_model(UpperCAmelCase__ ,			fx_model.params)
                         A__			,							A__							=   pt_inputs['''input_ids'''].shape
                         A__							=   np.random.randint(0 ,			seq_length - 1 ,			size=(batch_size,))
                         for batch_idx, start_index in enumerate(UpperCAmelCase__):
                                A__							=   0
                                A__							=   1
                                A__							=   0
                                A__							=   1

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

                         with torch.no_grad():
                                A__							=   pt_model(**UpperCAmelCase__).to_tuple()

                         A__							=   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__)
                                A__							=   pt_model_class.from_pretrained(UpperCAmelCase__ ,			from_flax=UpperCAmelCase__)

                         with torch.no_grad():
                                A__							=   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 	SCREAMING_SNAKE_CASE							(      self				:     Tuple)						->Optional[Any]:





           '''simple docstring'''

           for model_class_name in self.all_model_classes:
                  A__							=   model_class_name.from_pretrained('''EleutherAI/gpt-j-6B''')
                  A__							=   model(np.ones((1, 1)))
                  self.assertIsNotNone(UpperCAmelCase__)


