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

   __lowerCamelCase			: Dict							   =		'''0.12'''  # assumed parallelism: 8


if is_torch_available():
   import torch



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Dict ,     __UpperCamelCase    :       Any ,     __UpperCamelCase    :       int=None	)							->					Optional[Any]:







  """simple docstring"""





  if rng is None:
    SCREAMING_SNAKE_CASE__										=      random.Random()

  SCREAMING_SNAKE_CASE__										=      1
  for dim in shape:
    total_dims *= dim

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

  SCREAMING_SNAKE_CASE__										=      np.array(lowerCAmelCase_ ,     dtype=jnp.intaa	).reshape(lowerCAmelCase_	)

  return output



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Tuple ,     __UpperCamelCase    :       Optional[Any]=None	)							->					Union[str, Any]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      ids_tensor(lowerCAmelCase_ ,     vocab_size=2 ,     rng=lowerCAmelCase_	)
  # make sure that at least one token is attended to for each batch
  SCREAMING_SNAKE_CASE__										=      1
  return attn_mask







@require_flax
class __snake_case				:
   lowerCAmelCase_														=							None
   lowerCAmelCase_														=							()




   def 	__a						(    self			:				List[Any]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      self.model_tester.prepare_config_and_inputs_for_common()

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

     SCREAMING_SNAKE_CASE__										=      jnp.ones_like(_UpperCAmelCase      )
     SCREAMING_SNAKE_CASE__										=      attention_mask[:max_batch_size, :sequence_length]

     # generate max 5 tokens
     SCREAMING_SNAKE_CASE__										=      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()`
       SCREAMING_SNAKE_CASE__										=      config.eos_token_id
     return config, input_ids, attention_mask, max_length




   @is_pt_flax_cross_test
   def 	__a						(    self			:				Tuple      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      self._get_input_ids_and_config()
     SCREAMING_SNAKE_CASE__										=      False
     SCREAMING_SNAKE_CASE__										=      max_length
     SCREAMING_SNAKE_CASE__										=      0

     for model_class in self.all_generative_model_classes:
       SCREAMING_SNAKE_CASE__										=      model_class(_UpperCAmelCase      )

       SCREAMING_SNAKE_CASE__										=      model_class.__name__[4:]  # Skip the "Flax" at the beginning
       SCREAMING_SNAKE_CASE__										=      getattr(_UpperCAmelCase       ,	_UpperCAmelCase      )
       SCREAMING_SNAKE_CASE__										=      pt_model_class(_UpperCAmelCase      ).eval()
       SCREAMING_SNAKE_CASE__										=      load_flax_weights_in_pytorch_model(_UpperCAmelCase       ,	flax_model.params      )

       SCREAMING_SNAKE_CASE__										=      flax_model.generate(_UpperCAmelCase      ).sequences
       SCREAMING_SNAKE_CASE__										=      pt_model.generate(torch.tensor(_UpperCAmelCase       ,	dtype=torch.long      )      )

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

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




   def 	__a						(    self			:				Any      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      self._get_input_ids_and_config()
     SCREAMING_SNAKE_CASE__										=      False
     SCREAMING_SNAKE_CASE__										=      max_length

     for model_class in self.all_generative_model_classes:
       SCREAMING_SNAKE_CASE__										=      model_class(_UpperCAmelCase      )

       SCREAMING_SNAKE_CASE__										=      model.generate(_UpperCAmelCase      ).sequences
       self.assertEqual(generation_outputs.shape[-1]       ,	_UpperCAmelCase      )

       SCREAMING_SNAKE_CASE__										=      jit(model.generate      )
       SCREAMING_SNAKE_CASE__										=      jit_generate(_UpperCAmelCase      ).sequences

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




   def 	__a						(    self			:				List[str]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      self._get_input_ids_and_config()
     SCREAMING_SNAKE_CASE__										=      True
     SCREAMING_SNAKE_CASE__										=      max_length

     for model_class in self.all_generative_model_classes:
       SCREAMING_SNAKE_CASE__										=      model_class(_UpperCAmelCase      )

       SCREAMING_SNAKE_CASE__										=      model.generate(_UpperCAmelCase      ).sequences
       self.assertEqual(generation_outputs.shape[-1]       ,	_UpperCAmelCase      )

       SCREAMING_SNAKE_CASE__										=      jit(model.generate      )
       SCREAMING_SNAKE_CASE__										=      jit_generate(_UpperCAmelCase      ).sequences

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




   def 	__a						(    self			:				Tuple      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      self._get_input_ids_and_config()
     SCREAMING_SNAKE_CASE__										=      False
     SCREAMING_SNAKE_CASE__										=      max_length
     SCREAMING_SNAKE_CASE__										=      2

     for model_class in self.all_generative_model_classes:
       SCREAMING_SNAKE_CASE__										=      model_class(_UpperCAmelCase      )

       SCREAMING_SNAKE_CASE__										=      model.generate(_UpperCAmelCase      ).sequences
       self.assertEqual(generation_outputs.shape[-1]       ,	_UpperCAmelCase      )

       SCREAMING_SNAKE_CASE__										=      jit(model.generate      )
       SCREAMING_SNAKE_CASE__										=      jit_generate(_UpperCAmelCase      ).sequences

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




   def 	__a						(    self			:				Optional[Any]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      self._get_input_ids_and_config()
     SCREAMING_SNAKE_CASE__										=      False
     SCREAMING_SNAKE_CASE__										=      max_length
     SCREAMING_SNAKE_CASE__										=      2
     SCREAMING_SNAKE_CASE__										=      2

     for model_class in self.all_generative_model_classes:
       SCREAMING_SNAKE_CASE__										=      model_class(_UpperCAmelCase      )

       SCREAMING_SNAKE_CASE__										=      model.generate(_UpperCAmelCase      ).sequences
       self.assertEqual(generation_outputs.shape[0]       ,	input_ids.shape[0] * config.num_return_sequences      )




   def 	__a						(    self			:				str      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      self._get_input_ids_and_config()
     SCREAMING_SNAKE_CASE__										=      True
     SCREAMING_SNAKE_CASE__										=      max_length
     SCREAMING_SNAKE_CASE__										=      0.8
     SCREAMING_SNAKE_CASE__										=      10
     SCREAMING_SNAKE_CASE__										=      0.3
     SCREAMING_SNAKE_CASE__										=      1
     SCREAMING_SNAKE_CASE__										=      8
     SCREAMING_SNAKE_CASE__										=      9

     for model_class in self.all_generative_model_classes:
       SCREAMING_SNAKE_CASE__										=      model_class(_UpperCAmelCase      )

       SCREAMING_SNAKE_CASE__										=      model.generate(_UpperCAmelCase      ).sequences
       self.assertEqual(generation_outputs.shape[-1]       ,	_UpperCAmelCase      )

       SCREAMING_SNAKE_CASE__										=      jit(model.generate      )
       SCREAMING_SNAKE_CASE__										=      jit_generate(_UpperCAmelCase      ).sequences

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




   def 	__a						(    self			:				int      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      self._get_input_ids_and_config()
     SCREAMING_SNAKE_CASE__										=      max_length
     SCREAMING_SNAKE_CASE__										=      1
     SCREAMING_SNAKE_CASE__										=      8
     SCREAMING_SNAKE_CASE__										=      9

     for model_class in self.all_generative_model_classes:
       SCREAMING_SNAKE_CASE__										=      model_class(_UpperCAmelCase      )

       SCREAMING_SNAKE_CASE__										=      model.generate(_UpperCAmelCase      ).sequences
       self.assertEqual(generation_outputs.shape[-1]       ,	_UpperCAmelCase      )

       SCREAMING_SNAKE_CASE__										=      jit(model.generate      )
       SCREAMING_SNAKE_CASE__										=      jit_generate(_UpperCAmelCase      ).sequences

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




   def 	__a						(    self			:				Optional[int]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      self._get_input_ids_and_config()
     SCREAMING_SNAKE_CASE__										=      max_length
     SCREAMING_SNAKE_CASE__										=      2
     SCREAMING_SNAKE_CASE__										=      1
     SCREAMING_SNAKE_CASE__										=      8
     SCREAMING_SNAKE_CASE__										=      9

     for model_class in self.all_generative_model_classes:
       SCREAMING_SNAKE_CASE__										=      model_class(_UpperCAmelCase      )

       SCREAMING_SNAKE_CASE__										=      model.generate(_UpperCAmelCase      ).sequences
       self.assertEqual(generation_outputs.shape[-1]       ,	_UpperCAmelCase      )

       SCREAMING_SNAKE_CASE__										=      jit(model.generate      )
       SCREAMING_SNAKE_CASE__										=      jit_generate(_UpperCAmelCase      ).sequences

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




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



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      self._get_input_ids_and_config()

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

     SCREAMING_SNAKE_CASE__										=      False
     SCREAMING_SNAKE_CASE__										=      max_length

     for model_class in self.all_generative_model_classes:
       SCREAMING_SNAKE_CASE__										=      model_class(_UpperCAmelCase      )

       SCREAMING_SNAKE_CASE__										=      model.generate(_UpperCAmelCase       ,	attention_mask=_UpperCAmelCase      ).sequences
       self.assertEqual(generation_outputs.shape[-1]       ,	_UpperCAmelCase      )

       SCREAMING_SNAKE_CASE__										=      jit(model.generate      )
       SCREAMING_SNAKE_CASE__										=      jit_generate(_UpperCAmelCase       ,	attention_mask=_UpperCAmelCase      ).sequences

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




   def 	__a						(    self			:				Optional[Any]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      self._get_input_ids_and_config()

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

     SCREAMING_SNAKE_CASE__										=      True
     SCREAMING_SNAKE_CASE__										=      max_length

     for model_class in self.all_generative_model_classes:
       SCREAMING_SNAKE_CASE__										=      model_class(_UpperCAmelCase      )

       SCREAMING_SNAKE_CASE__										=      model.generate(_UpperCAmelCase       ,	attention_mask=_UpperCAmelCase      ).sequences
       self.assertEqual(generation_outputs.shape[-1]       ,	_UpperCAmelCase      )

       SCREAMING_SNAKE_CASE__										=      jit(model.generate      )
       SCREAMING_SNAKE_CASE__										=      jit_generate(_UpperCAmelCase       ,	attention_mask=_UpperCAmelCase      ).sequences

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




   def 	__a						(    self			:				Optional[int]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      self._get_input_ids_and_config()

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

     SCREAMING_SNAKE_CASE__										=      2
     SCREAMING_SNAKE_CASE__										=      max_length

     for model_class in self.all_generative_model_classes:
       SCREAMING_SNAKE_CASE__										=      model_class(_UpperCAmelCase      )

       SCREAMING_SNAKE_CASE__										=      model.generate(_UpperCAmelCase       ,	attention_mask=_UpperCAmelCase      ).sequences
       self.assertEqual(generation_outputs.shape[-1]       ,	_UpperCAmelCase      )

       SCREAMING_SNAKE_CASE__										=      jit(model.generate      )
       SCREAMING_SNAKE_CASE__										=      jit_generate(_UpperCAmelCase       ,	attention_mask=_UpperCAmelCase      ).sequences

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







@require_flax
class __snake_case				(						unittest.TestCase		):




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



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-bert"""      )
     SCREAMING_SNAKE_CASE__										=      FlaxAutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-bert-flax-only"""      )

     SCREAMING_SNAKE_CASE__										=      'Hello world'
     SCREAMING_SNAKE_CASE__										=      tokenizer(_UpperCAmelCase       ,	return_tensors="""np"""      ).input_ids

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

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





'''simple docstring'''

import os
import unittest

from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class 	__magic_name__						(    _UpperCamelCase			,						unittest.TestCase   ):
							lowerCAmelCase       :    str   				=					LayoutLMTokenizer
							lowerCAmelCase       :    Tuple   				=					LayoutLMTokenizerFast
							lowerCAmelCase       :    List[Any]   				=					True
							lowerCAmelCase       :    int   				=					True







							def 							__lowercase			(							self					:       Dict		):
									super().setUp()

									_a     :      int        =							[
									    '[UNK]',
									    '[CLS]',
									    '[SEP]',
									    'want',
									    '##want',
									    '##ed',
									    'wa',
									    'un',
									    'runn',
									    '##ing',
									    ',',
									    'low',
									    'lowest',
									]
									_a     :      List[str]        =							os.path.join(self.tmpdirname	,VOCAB_FILES_NAMES['vocab_file']		)
									with open(self.vocab_file	,'w'	,encoding='utf-8'		) as vocab_writer:
											vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]		)		)







							def 							__lowercase			(							self					:       Dict	,**_UpperCAmelCase					:       List[str]		):
									return LayoutLMTokenizer.from_pretrained(self.tmpdirname	,**_UpperCAmelCase		)







							def 							__lowercase			(							self					:       Optional[Any]	,_UpperCAmelCase					:       Tuple		):
									_a     :      Optional[int]        =							'UNwant\u00E9d,running'
									_a     :      List[Any]        =							'unwanted, running'
									return input_text, output_text







							def 							__lowercase			(							self					:       Optional[int]		):
									_a     :      Optional[Any]        =							self.tokenizer_class(self.vocab_file		)

									_a     :      Optional[Any]        =							tokenizer.tokenize('UNwant\u00E9d,running'		)
									self.assertListEqual(_UpperCAmelCase	,['un', '##want', '##ed', ',', 'runn', '##ing']		)
									self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCAmelCase		)	,[7, 4, 5, 10, 8, 9]		)







							def 							__lowercase			(							self					:       Optional[int]		):
									pass




