





'''simple docstring'''


import os
import sys
import warnings
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union

import numpy as np
import pyarrow as pa

from .. import config
from ..download.streaming_download_manager import xopen
from ..table import array_cast
from ..utils.file_utils import is_local_path
from ..utils.py_utils import first_non_null_value, no_op_if_value_is_null, string_to_dict


if TYPE_CHECKING:
 import PIL.Image

 from .features import FeatureType


lowerCAmelCase					:     Union[str, Any]												=None
lowerCAmelCase					:     Any												='''<''' if sys.byteorder == '''little''' else '''>'''
# Origin: https://github.com/python-pillow/Pillow/blob/698951e19e19972aeed56df686868f1329981c12/src/PIL/Image.py#L3126 minus "|i1" which values are not preserved correctly when saving and loading an image
lowerCAmelCase					:     List[str]												=[
    np.dtype('''|b1'''),
    np.dtype('''|u1'''),
    np.dtype('''<u2'''),
    np.dtype('''>u2'''),
    np.dtype('''<i2'''),
    np.dtype('''>i2'''),
    np.dtype('''<u4'''),
    np.dtype('''>u4'''),
    np.dtype('''<i4'''),
    np.dtype('''>i4'''),
    np.dtype('''<f4'''),
    np.dtype('''>f4'''),
    np.dtype('''<f8'''),
    np.dtype('''>f8'''),
]
@dataclass
class       a_   :
     __A      =			True
     __A      =			None
     # Automatically constructed
     __A      =			"PIL.Image.Image"
     __A      =			pa.struct({"bytes": pa.binary(), "path": pa.string()}							)
     __A      =			field(default="Image"	,						init=SCREAMING_SNAKE_CASE__	,						repr=SCREAMING_SNAKE_CASE__							)





     def __call__( self		:					int						):





        """simple docstring"""




        return self.pa_type





     def 							lowercase__      ( self		:					List[str]	,      lowercase		:					Tuple						):





        """simple docstring"""




        if config.PIL_AVAILABLE:
           import PIL.Image
        else:
           raise ImportError("To support encoding images, please install \'Pillow\'."						)

        if isinstance(__lowerCamelCase	,      __lowerCamelCase						):
           lowercase_			:List[str]    			= np.array(__lowerCamelCase						)

        if isinstance(__lowerCamelCase	,      __lowerCamelCase						):
           return {"path": value, "bytes": None}
        elif isinstance(__lowerCamelCase	,      __lowerCamelCase						):
           return {"path": None, "bytes": value}
        elif isinstance(__lowerCamelCase	,      np.ndarray						):
           # convert the image array to PNG/TIFF bytes
           return encode_np_array(__lowerCamelCase						)
        elif isinstance(__lowerCamelCase	,      PIL.Image.Image						):
           # convert the PIL image to bytes (default format is PNG/TIFF)
           return encode_pil_image(__lowerCamelCase						)
        elif value.get("path"						) is not None and os.path.isfile(value["path"]						):
           # we set "bytes": None to not duplicate the data if they're already available locally
           return {"bytes": None, "path": value.get("path"						)}
        elif value.get("bytes"						) is not None or value.get("path"						) is not None:
           # store the image bytes, and path is used to infer the image format using the file extension
           return {"bytes": value.get("bytes"						), "path": value.get("path"						)}
        else:
           raise ValueError(
               F'An image sample should have one of \'path\' or \'bytes\' but they are missing or None in {value}.'						)





     def 							lowercase__      ( self		:					str	,      lowercase		:					Union[str, Any]	,      lowercase		:					Optional[int]=None						):





        """simple docstring"""




        if not self.decode:
           raise RuntimeError("Decoding is disabled for this feature. Please use Image(decode=True) instead."						)

        if config.PIL_AVAILABLE:
           import PIL.Image
        else:
           raise ImportError("To support decoding images, please install \'Pillow\'."						)

        if token_per_repo_id is None:
           lowercase_			:Dict    			= {}

        lowercase_			:int    			= value['''path'''], value['''bytes''']
        if bytes_ is None:
           if path is None:
              raise ValueError(F'An image should have one of \'path\' or \'bytes\' but both are None in {value}.'						)
           else:
              if is_local_path(__lowerCamelCase						):
                 lowercase_			:Optional[int]    			= PIL.Image.open(__lowerCamelCase						)
              else:
                 lowercase_			:Any    			= path.split("::"						)[-1]
                 try:
                    lowercase_			:int    			= string_to_dict(__lowerCamelCase	,      config.HUB_DATASETS_URL						)['''repo_id''']
                    lowercase_			:List[Any]    			= token_per_repo_id.get(__lowerCamelCase						)
                 except ValueError:
                    lowercase_			:str    			= None
                 with xopen(__lowerCamelCase	,      "rb"	,      use_auth_token=__lowerCamelCase						) as f:
                    lowercase_			:Dict    			= BytesIO(f.read()						)
                 lowercase_			:Optional[Any]    			= PIL.Image.open(bytes_						)
        else:
           lowercase_			:Union[str, Any]    			= PIL.Image.open(BytesIO(bytes_						)						)
        image.load()  # to avoid "Too many open files" errors
        return image





     def 							lowercase__      ( self		:					Union[str, Any]						):





        """simple docstring"""




        from .features import Value

        return (
            self
            if self.decode
            else {
                "bytes": Value("binary"						),
                "path": Value("string"						),
            }
        )





     def 							lowercase__      ( self		:					Dict	,      lowercase		:					List[str]						):





        """simple docstring"""




        if pa.types.is_string(storage.type						):
           lowercase_			:str    			= pa.array([None] * len(__lowerCamelCase						)	,      type=pa.binary()						)
           lowercase_			:Optional[int]    			= pa.StructArray.from_arrays([bytes_array, storage]	,      ["bytes", "path"]	,      mask=storage.is_null()						)
        elif pa.types.is_binary(storage.type						):
           lowercase_			:Optional[Any]    			= pa.array([None] * len(__lowerCamelCase						)	,      type=pa.string()						)
           lowercase_			:str    			= pa.StructArray.from_arrays([storage, path_array]	,      ["bytes", "path"]	,      mask=storage.is_null()						)
        elif pa.types.is_struct(storage.type						):
           if storage.type.get_field_index("bytes"						) >= 0:
              lowercase_			:Any    			= storage.field("bytes"						)
           else:
              lowercase_			:Optional[int]    			= pa.array([None] * len(__lowerCamelCase						)	,      type=pa.binary()						)
           if storage.type.get_field_index("path"						) >= 0:
              lowercase_			:str    			= storage.field("path"						)
           else:
              lowercase_			:Optional[int]    			= pa.array([None] * len(__lowerCamelCase						)	,      type=pa.string()						)
           lowercase_			:Tuple    			= pa.StructArray.from_arrays([bytes_array, path_array]	,      ["bytes", "path"]	,      mask=storage.is_null()						)
        elif pa.types.is_list(storage.type						):
           lowercase_			:str    			= pa.array(
               [encode_np_array(np.array(__lowerCamelCase						)						)["bytes"] if arr is not None else None for arr in storage.to_pylist()]	,      type=pa.binary()	,      )
           lowercase_			:int    			= pa.array([None] * len(__lowerCamelCase						)	,      type=pa.string()						)
           lowercase_			:Dict    			= pa.StructArray.from_arrays(
               [bytes_array, path_array]	,      ["bytes", "path"]	,      mask=bytes_array.is_null()						)
        return array_cast(__lowerCamelCase	,      self.pa_type						)







     def 							lowercase__      ( self		:					List[Any]	,      lowercase		:					Optional[int]						):





        """simple docstring"""




        @no_op_if_value_is_null
        def path_to_bytes(lowercase		:					List[Any]						):
           with xopen(__lowerCamelCase	,      "rb"						) as f:
              lowercase_			:Optional[int]    			= f.read()
           return bytes_

        lowercase_			:Any    			= pa.array(
            [
                (path_to_bytes(x["path"]						) if x["bytes"] is None else x["bytes"]) if x is not None else None
                for x in storage.to_pylist()
            ]	,      type=pa.binary()	,      )
        lowercase_			:List[Any]    			= pa.array(
            [os.path.basename(__lowerCamelCase						) if path is not None else None for path in storage.field("path"						).to_pylist()]	,      type=pa.string()	,      )
        lowercase_			:Optional[Any]    			= pa.StructArray.from_arrays([bytes_array, path_array]	,      ["bytes", "path"]	,      mask=bytes_array.is_null()						)
        return array_cast(__lowerCamelCase	,      self.pa_type						)







def 							UpperCAmelCase_  (						):







   if config.PIL_AVAILABLE:
      import PIL.Image
   else:
      raise ImportError("To support encoding images, please install \'Pillow\'." )

   global _IMAGE_COMPRESSION_FORMATS
   if _IMAGE_COMPRESSION_FORMATS is None:
      PIL.Image.init()
      lowercase_			:Tuple    			= list(set(PIL.Image.OPEN.keys() ) & set(PIL.Image.SAVE.keys() ) )
   return _IMAGE_COMPRESSION_FORMATS
def 							UpperCAmelCase_  (						__lowerCamelCase					:					"PIL.Image.Image" ):
   lowercase_			:Tuple    			= BytesIO()
   if image.format in list_image_compression_formats():
      lowercase_			:str    			= image.format
   else:
      lowercase_			:Optional[int]    			= '''PNG''' if image.mode in ['''1''', '''L''', '''LA''', '''RGB''', '''RGBA'''] else '''TIFF'''
   image.save(snake_case_	,format=snake_case_ )
   return buffer.getvalue()
def 							UpperCAmelCase_  (						__lowerCamelCase					:					"PIL.Image.Image" ):







   if hasattr(snake_case_	,"filename" ) and image.filename != "":
      return {"path": image.filename, "bytes": None}
   else:
      return {"path": None, "bytes": image_to_bytes(snake_case_ )}
def 							UpperCAmelCase_  (						__lowerCamelCase					:					np.ndarray ):







   if config.PIL_AVAILABLE:
      import PIL.Image
   else:
      raise ImportError("To support encoding images, please install \'Pillow\'." )

   lowercase_			:Optional[Any]    			= array.dtype
   lowercase_			:List[str]    			= dtype.byteorder if dtype.byteorder != '''=''' else _NATIVE_BYTEORDER
   lowercase_			:int    			= dtype.kind
   lowercase_			:Any    			= dtype.itemsize

   lowercase_			:List[Any]    			= None

   # Multi-channel array case (only np.dtype("|u1") is allowed)
   if array.shape[2:]:
      lowercase_			:List[Any]    			= np.dtype("|u1" )
      if dtype_kind not in ["u", "i"]:
         raise TypeError(
             F'Unsupported array dtype {dtype} for image encoding. Only {dest_dtype} is supported for multi-channel arrays.' )
      if dtype is not dest_dtype:
         warnings.warn(F'Downcasting array dtype {dtype} to {dest_dtype} to be compatible with \'Pillow\'' )
    # Exact match
   elif dtype in _VALID_IMAGE_ARRAY_DTPYES:
      lowercase_			:str    			= dtype
   else:  # Downcast the type within the kind (np.can_cast(from_type, to_type, casting="same_kind") doesn't behave as expected, so do it manually)
      while dtype_itemsize >= 1:
         lowercase_			:Union[str, Any]    			= dtype_byteorder + dtype_kind + str(snake_case_ )
         lowercase_			:Tuple    			= np.dtype(snake_case_ )
         if dest_dtype in _VALID_IMAGE_ARRAY_DTPYES:
            warnings.warn(F'Downcasting array dtype {dtype} to {dest_dtype} to be compatible with \'Pillow\'' )
            break
         else:
            dtype_itemsize //= 2

   if dest_dtype is None:
      raise TypeError(
          F'Cannot convert dtype {dtype} to a valid image dtype. Valid image dtypes: {_VALID_IMAGE_ARRAY_DTPYES}' )
   lowercase_			:Optional[Any]    			= PIL.Image.fromarray(array.astype(snake_case_ ) )
   return {"path": None, "bytes": image_to_bytes(snake_case_ )}


def 							UpperCAmelCase_  (						__lowerCamelCase					:					Union[List[str], List[dict], List[np.ndarray], List["PIL.Image.Image"]] ):







   if config.PIL_AVAILABLE:
      import PIL.Image
   else:
      raise ImportError("To support encoding images, please install \'Pillow\'." )

   if objs:
      lowercase_			:Optional[int]    			= first_non_null_value(snake_case_ )
      if isinstance(snake_case_	,snake_case_ ):
         return [{"path": obj, "bytes": None} if obj is not None else None for obj in objs]
      if isinstance(snake_case_	,np.ndarray ):
         lowercase_			:str    			= no_op_if_value_is_null(snake_case_ )
         return [obj_to_image_dict_func(snake_case_ ) for obj in objs]
      elif isinstance(snake_case_	,PIL.Image.Image ):
         lowercase_			:Any    			= no_op_if_value_is_null(snake_case_ )
         return [obj_to_image_dict_func(snake_case_ ) for obj in objs]
      else:
         return objs
   else:
      return objs









"""simple docstring"""



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

       a_ =			"""0.12"""  # assumed parallelism: 8


if is_torch_available():
       import torch







def 		__lowercase     (   snake_case_  :  Any				,snake_case_  :  int				,snake_case_  :  List[str]=None						)   ->Union[str, Any]:



    '''simple docstring'''







    if rng is None:
        __A    :						Union[str, Any]						 =     random.Random()

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

    __A    :						List[str]						 =     []
    for _ in range(snake_case_						):
        values.append(rng.randint(0				,vocab_size - 1						)						)

    __A    :						Optional[Any]						 =     np.array(snake_case_				,dtype=jnp.intaa						).reshape(snake_case_						)

    return output







def 		__lowercase     (   snake_case_  :  int				,snake_case_  :  Union[str, Any]=None						)   ->int:



    '''simple docstring'''







    __A    :						int						 =     ids_tensor(snake_case_				,vocab_size=2				,rng=snake_case_						)
    # make sure that at least one token is attended to for each batch
    __A    :						str						 =     1
    return attn_mask






@require_flax
class 		__snake_case					:





 """simple docstring"""


 _lowerCamelCase        =     None
 _lowerCamelCase        =     ()

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A ,       __A    :						Optional[int]						 =     self.model_tester.prepare_config_and_inputs_for_common()

     # cut to half length & take max batch_size 3
     __A    :						Optional[int]						 =     2
     __A    :						Tuple						 =     inputs['''input_ids'''].shape[-1] // 2
     __A    :						List[Any]						 =     inputs['''input_ids'''][:max_batch_size, :sequence_length]

     __A    :						Optional[Any]						 =     jnp.ones_like(__lowerCamelCase  )
     __A    :						List[Any]						 =     attention_mask[:max_batch_size, :sequence_length]

     # generate max 5 tokens
     __A    :						Optional[int]						 =     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()`
         __A    :						Optional[int]						 =     config.eos_token_id
     return config, input_ids, attention_mask, max_length

 @is_pt_flax_cross_test
 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A ,       __A ,       __A ,       __A    :						List[Any]						 =     self._get_input_ids_and_config()
     __A    :						List[Any]						 =     False
     __A    :						str						 =     max_length
     __A    :						Tuple						 =     0

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

         __A    :						Any						 =     model_class.__name__[4:]  # Skip the "Flax" at the beginning
         __A    :						Dict						 =     getattr(__lowerCamelCase					,						__lowerCamelCase  )
         __A    :						Tuple						 =     pt_model_class(__lowerCamelCase  ).eval()
         __A    :						Optional[Any]						 =     load_flax_weights_in_pytorch_model(__lowerCamelCase					,						flax_model.params  )

         __A    :						str						 =     flax_model.generate(__lowerCamelCase  ).sequences
         __A    :						List[Any]						 =     pt_model.generate(torch.tensor(__lowerCamelCase					,						dtype=torch.long  )  )

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

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

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A ,       __A ,       __A ,       __A    :						Union[str, Any]						 =     self._get_input_ids_and_config()
     __A    :						List[str]						 =     False
     __A    :						Optional[Any]						 =     max_length

     for model_class in self.all_generative_model_classes:
         __A    :						Optional[int]						 =     model_class(__lowerCamelCase  )

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

         __A    :						Union[str, Any]						 =     jit(model.generate  )
         __A    :						str						 =     jit_generate(__lowerCamelCase  ).sequences

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

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A ,       __A ,       __A ,       __A    :						int						 =     self._get_input_ids_and_config()
     __A    :						List[Any]						 =     True
     __A    :						Union[str, Any]						 =     max_length

     for model_class in self.all_generative_model_classes:
         __A    :						Dict						 =     model_class(__lowerCamelCase  )

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

         __A    :						List[Any]						 =     jit(model.generate  )
         __A    :						List[str]						 =     jit_generate(__lowerCamelCase  ).sequences

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

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A ,       __A ,       __A ,       __A    :						List[Any]						 =     self._get_input_ids_and_config()
     __A    :						List[str]						 =     False
     __A    :						Any						 =     max_length
     __A    :						List[Any]						 =     2

     for model_class in self.all_generative_model_classes:
         __A    :						Tuple						 =     model_class(__lowerCamelCase  )

         __A    :						Optional[Any]						 =     model.generate(__lowerCamelCase  ).sequences
         self.assertEqual(generation_outputs.shape[-1]					,						__lowerCamelCase  )

         __A    :						Union[str, Any]						 =     jit(model.generate  )
         __A    :						Optional[int]						 =     jit_generate(__lowerCamelCase  ).sequences

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

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A ,       __A ,       __A ,       __A    :						Tuple						 =     self._get_input_ids_and_config()
     __A    :						str						 =     False
     __A    :						Union[str, Any]						 =     max_length
     __A    :						List[Any]						 =     2
     __A    :						Any						 =     2

     for model_class in self.all_generative_model_classes:
         __A    :						List[str]						 =     model_class(__lowerCamelCase  )

         __A    :						Optional[int]						 =     model.generate(__lowerCamelCase  ).sequences
         self.assertEqual(generation_outputs.shape[0]					,						input_ids.shape[0] * config.num_return_sequences  )

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




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

     for model_class in self.all_generative_model_classes:
         __A    :						List[Any]						 =     model_class(__lowerCamelCase  )

         __A    :						List[str]						 =     model.generate(__lowerCamelCase  ).sequences
         self.assertEqual(generation_outputs.shape[-1]					,						__lowerCamelCase  )

         __A    :						Any						 =     jit(model.generate  )
         __A    :						str						 =     jit_generate(__lowerCamelCase  ).sequences

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

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A ,       __A ,       __A ,       __A    :						Union[str, Any]						 =     self._get_input_ids_and_config()
     __A    :						Union[str, Any]						 =     max_length
     __A    :						List[str]						 =     1
     __A    :						str						 =     8
     __A    :						Any						 =     9

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

         __A    :						Tuple						 =     model.generate(__lowerCamelCase  ).sequences
         self.assertEqual(generation_outputs.shape[-1]					,						__lowerCamelCase  )

         __A    :						Optional[Any]						 =     jit(model.generate  )
         __A    :						Optional[Any]						 =     jit_generate(__lowerCamelCase  ).sequences

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

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A ,       __A ,       __A ,       __A    :						int						 =     self._get_input_ids_and_config()
     __A    :						Optional[int]						 =     max_length
     __A    :						List[str]						 =     2
     __A    :						List[Any]						 =     1
     __A    :						Optional[Any]						 =     8
     __A    :						str						 =     9

     for model_class in self.all_generative_model_classes:
         __A    :						Optional[int]						 =     model_class(__lowerCamelCase  )

         __A    :						int						 =     model.generate(__lowerCamelCase  ).sequences
         self.assertEqual(generation_outputs.shape[-1]					,						__lowerCamelCase  )

         __A    :						str						 =     jit(model.generate  )
         __A    :						Any						 =     jit_generate(__lowerCamelCase  ).sequences

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

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A ,       __A ,       __A ,       __A    :						int						 =     self._get_input_ids_and_config()

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

     __A    :						Optional[Any]						 =     False
     __A    :						Union[str, Any]						 =     max_length

     for model_class in self.all_generative_model_classes:
         __A    :						int						 =     model_class(__lowerCamelCase  )

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

         __A    :						Optional[int]						 =     jit(model.generate  )
         __A    :						Any						 =     jit_generate(__lowerCamelCase					,						attention_mask=__lowerCamelCase  ).sequences

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

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A ,       __A ,       __A ,       __A    :						Optional[int]						 =     self._get_input_ids_and_config()

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

     __A    :						List[Any]						 =     True
     __A    :						Any						 =     max_length

     for model_class in self.all_generative_model_classes:
         __A    :						str						 =     model_class(__lowerCamelCase  )

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

         __A    :						List[str]						 =     jit(model.generate  )
         __A    :						Optional[int]						 =     jit_generate(__lowerCamelCase					,						attention_mask=__lowerCamelCase  ).sequences

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







 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A ,       __A ,       __A ,       __A    :						Optional[Any]						 =     self._get_input_ids_and_config()

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

     __A    :						Optional[int]						 =     2
     __A    :						Dict						 =     max_length

     for model_class in self.all_generative_model_classes:
         __A    :						Any						 =     model_class(__lowerCamelCase  )

         __A    :						int						 =     model.generate(__lowerCamelCase					,						attention_mask=__lowerCamelCase  ).sequences
         self.assertEqual(generation_outputs.shape[-1]					,						__lowerCamelCase  )

         __A    :						str						 =     jit(model.generate  )
         __A    :						str						 =     jit_generate(__lowerCamelCase					,						attention_mask=__lowerCamelCase  ).sequences

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






@require_flax
class 		__snake_case					(		unittest.TestCase     ):





 """simple docstring"""


 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						Optional[int]						 =     AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-bert'''  )
     __A    :						Optional[Any]						 =     FlaxAutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-bert-flax-only'''  )

     __A    :						Any						 =     '''Hello world'''
     __A    :						Dict						 =     tokenizer(__lowerCamelCase					,						return_tensors='''np'''  ).input_ids

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

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




