

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    :Optional[int]      =			'0.12'  # assumed parallelism: 8


if is_torch_available():
      import torch

def       __snake_case						( __UpperCamelCase	: Any				,__UpperCamelCase	: Dict				,__UpperCamelCase	: Optional[int]=None    ):



      """simple docstring"""

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

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

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

      A_						  =				np.array(lowercase__				,dtype=jnp.intaa    ).reshape(lowercase__    )

      return output

def       __snake_case						( __UpperCamelCase	: int				,__UpperCamelCase	: List[Any]=None    ):



      """simple docstring"""

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

@require_flax
class   _a					:




      """simple docstring"""

      _lowerCamelCase       :			Dict      		=       None
      _lowerCamelCase       :			Dict      		=       ()





      def 		__A	(   self							:				Optional[Any]	):
            A_						  =				self.model_tester.prepare_config_and_inputs_for_common()

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

            A_						  =				jnp.ones_like(UpperCAmelCase	)
            A_						  =				attention_mask[:max_batch_size, :sequence_length]

            # generate max 5 tokens
            A_						  =				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_						  =				config.eos_token_id
            return config, input_ids, attention_mask, max_length





      @is_pt_flax_cross_test
      def 		__A	(   self							:				Dict	):
            A_						  =				self._get_input_ids_and_config()
            A_						  =				False
            A_						  =				max_length
            A_						  =				0

            for model_class in self.all_generative_model_classes:
                  A_						  =				model_class(UpperCAmelCase	)

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

                  A_						  =				flax_model.generate(UpperCAmelCase	).sequences
                  A_						  =				pt_model.generate(torch.tensor(UpperCAmelCase						,			dtype=torch.long	)	)

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

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





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

            for model_class in self.all_generative_model_classes:
                  A_						  =				model_class(UpperCAmelCase	)

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

                  A_						  =				jit(model.generate	)
                  A_						  =				jit_generate(UpperCAmelCase	).sequences

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





      def 		__A	(   self							:				int	):
            A_						  =				self._get_input_ids_and_config()
            A_						  =				True
            A_						  =				max_length

            for model_class in self.all_generative_model_classes:
                  A_						  =				model_class(UpperCAmelCase	)

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

                  A_						  =				jit(model.generate	)
                  A_						  =				jit_generate(UpperCAmelCase	).sequences

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





      def 		__A	(   self							:				str	):
            A_						  =				self._get_input_ids_and_config()
            A_						  =				False
            A_						  =				max_length
            A_						  =				2

            for model_class in self.all_generative_model_classes:
                  A_						  =				model_class(UpperCAmelCase	)

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

                  A_						  =				jit(model.generate	)
                  A_						  =				jit_generate(UpperCAmelCase	).sequences

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





      def 		__A	(   self							:				str	):
            A_						  =				self._get_input_ids_and_config()
            A_						  =				False
            A_						  =				max_length
            A_						  =				2
            A_						  =				2

            for model_class in self.all_generative_model_classes:
                  A_						  =				model_class(UpperCAmelCase	)

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





      def 		__A	(   self							:				List[str]	):
            A_						  =				self._get_input_ids_and_config()
            A_						  =				True
            A_						  =				max_length
            A_						  =				0.8
            A_						  =				10
            A_						  =				0.3
            A_						  =				1
            A_						  =				8
            A_						  =				9

            for model_class in self.all_generative_model_classes:
                  A_						  =				model_class(UpperCAmelCase	)

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

                  A_						  =				jit(model.generate	)
                  A_						  =				jit_generate(UpperCAmelCase	).sequences

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





      def 		__A	(   self							:				List[Any]	):
            A_						  =				self._get_input_ids_and_config()
            A_						  =				max_length
            A_						  =				1
            A_						  =				8
            A_						  =				9

            for model_class in self.all_generative_model_classes:
                  A_						  =				model_class(UpperCAmelCase	)

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

                  A_						  =				jit(model.generate	)
                  A_						  =				jit_generate(UpperCAmelCase	).sequences

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





      def 		__A	(   self							:				int	):
            A_						  =				self._get_input_ids_and_config()
            A_						  =				max_length
            A_						  =				2
            A_						  =				1
            A_						  =				8
            A_						  =				9

            for model_class in self.all_generative_model_classes:
                  A_						  =				model_class(UpperCAmelCase	)

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

                  A_						  =				jit(model.generate	)
                  A_						  =				jit_generate(UpperCAmelCase	).sequences

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





      def 		__A	(   self							:				int	):
            A_						  =				self._get_input_ids_and_config()

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

            A_						  =				False
            A_						  =				max_length

            for model_class in self.all_generative_model_classes:
                  A_						  =				model_class(UpperCAmelCase	)

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

                  A_						  =				jit(model.generate	)
                  A_						  =				jit_generate(UpperCAmelCase						,			attention_mask=UpperCAmelCase	).sequences

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





      def 		__A	(   self							:				Any	):
            A_						  =				self._get_input_ids_and_config()

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

            A_						  =				True
            A_						  =				max_length

            for model_class in self.all_generative_model_classes:
                  A_						  =				model_class(UpperCAmelCase	)

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

                  A_						  =				jit(model.generate	)
                  A_						  =				jit_generate(UpperCAmelCase						,			attention_mask=UpperCAmelCase	).sequences

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







      def 		__A	(   self							:				Any	):
            A_						  =				self._get_input_ids_and_config()

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

            A_						  =				2
            A_						  =				max_length

            for model_class in self.all_generative_model_classes:
                  A_						  =				model_class(UpperCAmelCase	)

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

                  A_						  =				jit(model.generate	)
                  A_						  =				jit_generate(UpperCAmelCase						,			attention_mask=UpperCAmelCase	).sequences

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




@require_flax
class   _a					(			unittest.TestCase				):




      """simple docstring"""

      def 		__A	(   self							:				List[Any]	):
            A_						  =				AutoTokenizer.from_pretrained("hf-internal-testing/tiny-bert"	)
            A_						  =				FlaxAutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-bert-flax-only"	)

            A_						  =				"Hello world"
            A_						  =				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"	):
                  A_						  =				{"foo": "bar"}
                  model.generate(UpperCAmelCase						,			**UpperCAmelCase	)


"""simple docstring"""

import os
import unittest

from transformers import LxmertTokenizer, LxmertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class 			__a					(						__magic_name__       ,	unittest.TestCase       ):





       """simple docstring"""


       __UpperCamelCase					:      Optional[Any]    =						LxmertTokenizer
       __UpperCamelCase					:      Optional[Any]    =						LxmertTokenizerFast
       __UpperCamelCase					:      str    =						True
       __UpperCamelCase					:      Any    =						True






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        super().setUp()

        lowerCAmelCase__   :				Tuple 			=				[
            "[UNK]",
            "[CLS]",
            "[SEP]",
            "want",
            "##want",
            "##ed",
            "wa",
            "un",
            "runn",
            "##ing",
            ",",
            "low",
            "lowest",
        ]
        lowerCAmelCase__   :				Dict 			=				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 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case   ):





        """simple docstring"""

        lowerCAmelCase__   :				List[Any] 			=				"UNwant\u00E9d,running"
        lowerCAmelCase__   :				Union[str, Any] 			=				"unwanted, running"
        return input_text, output_text






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				str 			=				self.tokenizer_class(self.vocab_file   )

        lowerCAmelCase__   :				List[Any] 			=				tokenizer.tokenize("UNwant\u00E9d,running"   )
        self.assertListEqual(snake_case			,					["un", "##want", "##ed", ",", "runn", "##ing"]   )
        self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case   )			,					[7, 4, 5, 10, 8, 9]   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        if not self.test_rust_tokenizer:
         return

        lowerCAmelCase__   :				int 			=				self.get_tokenizer()
        lowerCAmelCase__   :				List[str] 			=				self.get_rust_tokenizer()

        lowerCAmelCase__   :				Optional[Any] 			=				"I was born in 92000, and this is falsé."

        lowerCAmelCase__   :				int 			=				tokenizer.tokenize(snake_case   )
        lowerCAmelCase__   :				int 			=				rust_tokenizer.tokenize(snake_case   )
        self.assertListEqual(snake_case			,					snake_case   )

        lowerCAmelCase__   :				str 			=				tokenizer.encode(snake_case			,					add_special_tokens=snake_case   )
        lowerCAmelCase__   :				List[str] 			=				rust_tokenizer.encode(snake_case			,					add_special_tokens=snake_case   )
        self.assertListEqual(snake_case			,					snake_case   )

        lowerCAmelCase__   :				Tuple 			=				self.get_rust_tokenizer()
        lowerCAmelCase__   :				Tuple 			=				tokenizer.encode(snake_case   )
        lowerCAmelCase__   :				List[Any] 			=				rust_tokenizer.encode(snake_case   )
        self.assertListEqual(snake_case			,					snake_case   )






