





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			       =      '0.12'  # assumed parallelism: 8


if is_torch_available():
       import torch




def    __UpperCAmelCase(      lowercase_ ,    lowercase_ ,    lowercase_=None      ):
       if rng is None:
              _lowerCamelCase							:     Union[str, Any]	=		random.Random()

       _lowerCamelCase							:     List[Any]	=		1
       for dim in shape:
              total_dims *= dim

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

       _lowerCamelCase							:     List[str]	=		np.array(lowercase_ ,    dtype=jnp.intaa      ).reshape(lowercase_      )

       return output




def    __UpperCAmelCase(      lowercase_ ,    lowercase_=None      ):
       _lowerCamelCase							:     Dict	=		ids_tensor(lowercase_ ,    vocab_size=2 ,    rng=lowercase_      )
       # make sure that at least one token is attended to for each batch
       _lowerCamelCase							:     int	=		1
       return attn_mask







@require_flax
class 			__A       :




      """simple docstring"""







      UpperCAmelCase__						=      None
      UpperCAmelCase__						=      ()


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase,  _lowerCamelCase							:     List[Any]	=		self.model_tester.prepare_config_and_inputs_for_common()

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

             _lowerCamelCase							:     Any	=		jnp.ones_like(a__)
             _lowerCamelCase							:     List[Any]	=		attention_mask[:max_batch_size, :sequence_length]

             # generate max 5 tokens
             _lowerCamelCase							:     Union[str, Any]	=		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()`
                    _lowerCamelCase							:     Union[str, Any]	=		config.eos_token_id
             return config, input_ids, attention_mask, max_length


      @is_pt_flax_cross_test
      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase							:     Any	=		self._get_input_ids_and_config()
             _lowerCamelCase							:     Optional[Any]	=		False
             _lowerCamelCase							:     Any	=		max_length
             _lowerCamelCase							:     Dict	=		0

             for model_class in self.all_generative_model_classes:
                    _lowerCamelCase							:     Optional[Any]	=		model_class(a__)

                    _lowerCamelCase							:     Optional[int]	=		model_class.__name__[4:]  # Skip the "Flax" at the beginning
                    _lowerCamelCase							:     Union[str, Any]	=		getattr(a__      ,							a__)
                    _lowerCamelCase							:     List[Any]	=		pt_model_class(a__).eval()
                    _lowerCamelCase							:     str	=		load_flax_weights_in_pytorch_model(a__      ,							flax_model.params)

                    _lowerCamelCase							:     int	=		flax_model.generate(a__).sequences
                    _lowerCamelCase							:     List[str]	=		pt_model.generate(torch.tensor(a__      ,							dtype=torch.long))

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

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


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase							:     Optional[Any]	=		self._get_input_ids_and_config()
             _lowerCamelCase							:     Dict	=		False
             _lowerCamelCase							:     Optional[Any]	=		max_length

             for model_class in self.all_generative_model_classes:
                    _lowerCamelCase							:     Optional[Any]	=		model_class(a__)

                    _lowerCamelCase							:     Any	=		model.generate(a__).sequences
                    self.assertEqual(generation_outputs.shape[-1]      ,							a__)

                    _lowerCamelCase							:     Tuple	=		jit(model.generate)
                    _lowerCamelCase							:     Tuple	=		jit_generate(a__).sequences

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


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase							:     Optional[Any]	=		self._get_input_ids_and_config()
             _lowerCamelCase							:     int	=		True
             _lowerCamelCase							:     str	=		max_length

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

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

                    _lowerCamelCase							:     Optional[int]	=		jit(model.generate)
                    _lowerCamelCase							:     List[Any]	=		jit_generate(a__).sequences

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


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase							:     List[Any]	=		self._get_input_ids_and_config()
             _lowerCamelCase							:     Dict	=		False
             _lowerCamelCase							:     Union[str, Any]	=		max_length
             _lowerCamelCase							:     Optional[Any]	=		2

             for model_class in self.all_generative_model_classes:
                    _lowerCamelCase							:     str	=		model_class(a__)

                    _lowerCamelCase							:     str	=		model.generate(a__).sequences
                    self.assertEqual(generation_outputs.shape[-1]      ,							a__)

                    _lowerCamelCase							:     Tuple	=		jit(model.generate)
                    _lowerCamelCase							:     Optional[int]	=		jit_generate(a__).sequences

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


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase							:     Dict	=		self._get_input_ids_and_config()
             _lowerCamelCase							:     List[Any]	=		False
             _lowerCamelCase							:     str	=		max_length
             _lowerCamelCase							:     Optional[Any]	=		2
             _lowerCamelCase							:     int	=		2

             for model_class in self.all_generative_model_classes:
                    _lowerCamelCase							:     Optional[int]	=		model_class(a__)

                    _lowerCamelCase							:     List[Any]	=		model.generate(a__).sequences
                    self.assertEqual(generation_outputs.shape[0]      ,							input_ids.shape[0] * config.num_return_sequences)


      def 				__snake_case			(		self):



             """simple docstring"""





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

             for model_class in self.all_generative_model_classes:
                    _lowerCamelCase							:     Optional[int]	=		model_class(a__)

                    _lowerCamelCase							:     Any	=		model.generate(a__).sequences
                    self.assertEqual(generation_outputs.shape[-1]      ,							a__)

                    _lowerCamelCase							:     List[str]	=		jit(model.generate)
                    _lowerCamelCase							:     Union[str, Any]	=		jit_generate(a__).sequences

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


      def 				__snake_case			(		self):



             """simple docstring"""





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

             for model_class in self.all_generative_model_classes:
                    _lowerCamelCase							:     str	=		model_class(a__)

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

                    _lowerCamelCase							:     Tuple	=		jit(model.generate)
                    _lowerCamelCase							:     List[Any]	=		jit_generate(a__).sequences

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


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase							:     Optional[Any]	=		self._get_input_ids_and_config()
             _lowerCamelCase							:     int	=		max_length
             _lowerCamelCase							:     Dict	=		2
             _lowerCamelCase							:     int	=		1
             _lowerCamelCase							:     int	=		8
             _lowerCamelCase							:     Optional[int]	=		9

             for model_class in self.all_generative_model_classes:
                    _lowerCamelCase							:     str	=		model_class(a__)

                    _lowerCamelCase							:     str	=		model.generate(a__).sequences
                    self.assertEqual(generation_outputs.shape[-1]      ,							a__)

                    _lowerCamelCase							:     int	=		jit(model.generate)
                    _lowerCamelCase							:     int	=		jit_generate(a__).sequences

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


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase							:     Any	=		self._get_input_ids_and_config()

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

             _lowerCamelCase							:     Optional[int]	=		False
             _lowerCamelCase							:     int	=		max_length

             for model_class in self.all_generative_model_classes:
                    _lowerCamelCase							:     Dict	=		model_class(a__)

                    _lowerCamelCase							:     List[str]	=		model.generate(a__      ,							attention_mask=a__).sequences
                    self.assertEqual(generation_outputs.shape[-1]      ,							a__)

                    _lowerCamelCase							:     str	=		jit(model.generate)
                    _lowerCamelCase							:     int	=		jit_generate(a__      ,							attention_mask=a__).sequences

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


      def 				__snake_case			(		self):



             """simple docstring"""





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

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

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

             for model_class in self.all_generative_model_classes:
                    _lowerCamelCase							:     Optional[Any]	=		model_class(a__)

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

                    _lowerCamelCase							:     Tuple	=		jit(model.generate)
                    _lowerCamelCase							:     str	=		jit_generate(a__      ,							attention_mask=a__).sequences

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



      def 				__snake_case			(		self):



             """simple docstring"""





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

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

             _lowerCamelCase							:     Optional[Any]	=		2
             _lowerCamelCase							:     Union[str, Any]	=		max_length

             for model_class in self.all_generative_model_classes:
                    _lowerCamelCase							:     str	=		model_class(a__)

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

                    _lowerCamelCase							:     str	=		jit(model.generate)
                    _lowerCamelCase							:     str	=		jit_generate(a__      ,							attention_mask=a__).sequences

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







@require_flax
class 			__A       (      unittest.TestCase  ):




      """simple docstring"""







      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     Tuple	=		AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-bert''')
             _lowerCamelCase							:     List[Any]	=		FlaxAutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-bert-flax-only''')

             _lowerCamelCase							:     Optional[int]	=		'''Hello world'''
             _lowerCamelCase							:     str	=		tokenizer(a__      ,							return_tensors='''np''').input_ids

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

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








import warnings

from .generation import TFGenerationMixin







class 			__A       (      lowerCamelCase__  ):




      """simple docstring"""







      warnings.warn(
          """Importing `TFGenerationMixin` from `src/transformers/generation_tf_utils.py` is deprecated and will """
          """be removed in Transformers v5. Import as `from transformers import TFGenerationMixin` instead.""" ,lowerCamelCase__ ,)

