





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

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


if is_torch_available():
							import torch





def  __magic_name__  (				lowercase				,       lowercase				,       lowercase=None				)				-> Dict:

							"""simple docstring"""



							if rng is None:
														lowercase_	:   Tuple  				=     random.Random()

							lowercase_	:   Tuple  				=     1
							for dim in shape:
														total_dims *= dim

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

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

							return output





def  __magic_name__  (				lowercase				,       lowercase=None				)				-> Optional[Any]:

							"""simple docstring"""



							lowercase_	:   Dict  				=     ids_tensor(lowercase				,       vocab_size=2				,       rng=lowercase				)
							# make sure that at least one token is attended to for each batch
							lowercase_	:   Dict  				=     1
							return attn_mask
@require_flax
class     UpperCamelCase__					:






		'''simple docstring'''






		__a       :       int       =	None
		__a       :       str       =	()






		def 						snake_case__				(							self  )       -> Any:






									"""simple docstring"""
									lowercase_				,		lowercase_	:   str  				=     self.model_tester.prepare_config_and_inputs_for_common()

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

									lowercase_	:   Union[str, Any]  				=     jnp.ones_like(snake_case__  )
									lowercase_	:   Union[str, Any]  				=     attention_mask[:max_batch_size, :sequence_length]

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






		@is_pt_flax_cross_test
		def 						snake_case__				(							self  )       -> Dict:






									"""simple docstring"""
									lowercase_				,		lowercase_				,		lowercase_				,		lowercase_	:   Optional[Any]  				=     self._get_input_ids_and_config()
									lowercase_	:   Union[str, Any]  				=     False
									lowercase_	:   Any  				=     max_length
									lowercase_	:   Optional[Any]  				=     0

									for model_class in self.all_generative_model_classes:
																lowercase_	:   List[str]  				=     model_class(snake_case__  )

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

																lowercase_	:   List[str]  				=     flax_model.generate(snake_case__  ).sequences
																lowercase_	:   List[str]  				=     pt_model.generate(torch.tensor(snake_case__,   dtype=torch.long  )  )

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

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






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






									"""simple docstring"""
									lowercase_				,		lowercase_				,		lowercase_				,		lowercase_	:   Any  				=     self._get_input_ids_and_config()
									lowercase_	:   int  				=     False
									lowercase_	:   Union[str, Any]  				=     max_length

									for model_class in self.all_generative_model_classes:
																lowercase_	:   Dict  				=     model_class(snake_case__  )

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

																lowercase_	:   Union[str, Any]  				=     jit(model.generate  )
																lowercase_	:   Tuple  				=     jit_generate(snake_case__  ).sequences

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






		def 						snake_case__				(							self  )       -> Optional[int]:






									"""simple docstring"""
									lowercase_				,		lowercase_				,		lowercase_				,		lowercase_	:   str  				=     self._get_input_ids_and_config()
									lowercase_	:   int  				=     True
									lowercase_	:   List[Any]  				=     max_length

									for model_class in self.all_generative_model_classes:
																lowercase_	:   str  				=     model_class(snake_case__  )

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

																lowercase_	:   Tuple  				=     jit(model.generate  )
																lowercase_	:   Union[str, Any]  				=     jit_generate(snake_case__  ).sequences

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






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






									"""simple docstring"""
									lowercase_				,		lowercase_				,		lowercase_				,		lowercase_	:   str  				=     self._get_input_ids_and_config()
									lowercase_	:   Union[str, Any]  				=     False
									lowercase_	:   Union[str, Any]  				=     max_length
									lowercase_	:   Any  				=     2

									for model_class in self.all_generative_model_classes:
																lowercase_	:   Dict  				=     model_class(snake_case__  )

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

																lowercase_	:   Optional[Any]  				=     jit(model.generate  )
																lowercase_	:   Dict  				=     jit_generate(snake_case__  ).sequences

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






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






									"""simple docstring"""
									lowercase_				,		lowercase_				,		lowercase_				,		lowercase_	:   int  				=     self._get_input_ids_and_config()
									lowercase_	:   int  				=     False
									lowercase_	:   Optional[int]  				=     max_length
									lowercase_	:   Optional[int]  				=     2
									lowercase_	:   Union[str, Any]  				=     2

									for model_class in self.all_generative_model_classes:
																lowercase_	:   Tuple  				=     model_class(snake_case__  )

																lowercase_	:   Union[str, Any]  				=     model.generate(snake_case__  ).sequences
																self.assertEqual(generation_outputs.shape[0],   input_ids.shape[0] * config.num_return_sequences  )






		def 						snake_case__				(							self  )       -> Tuple:






									"""simple docstring"""
									lowercase_				,		lowercase_				,		lowercase_				,		lowercase_	:   Tuple  				=     self._get_input_ids_and_config()
									lowercase_	:   List[str]  				=     True
									lowercase_	:   List[Any]  				=     max_length
									lowercase_	:   int  				=     0.8
									lowercase_	:   str  				=     10
									lowercase_	:   Any  				=     0.3
									lowercase_	:   List[Any]  				=     1
									lowercase_	:   str  				=     8
									lowercase_	:   Optional[Any]  				=     9

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

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

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

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






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






									"""simple docstring"""
									lowercase_				,		lowercase_				,		lowercase_				,		lowercase_	:   Optional[int]  				=     self._get_input_ids_and_config()
									lowercase_	:   Optional[Any]  				=     max_length
									lowercase_	:   List[Any]  				=     1
									lowercase_	:   Any  				=     8
									lowercase_	:   Tuple  				=     9

									for model_class in self.all_generative_model_classes:
																lowercase_	:   Optional[Any]  				=     model_class(snake_case__  )

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

																lowercase_	:   Any  				=     jit(model.generate  )
																lowercase_	:   Any  				=     jit_generate(snake_case__  ).sequences

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






		def 						snake_case__				(							self  )       -> str:






									"""simple docstring"""
									lowercase_				,		lowercase_				,		lowercase_				,		lowercase_	:   Dict  				=     self._get_input_ids_and_config()
									lowercase_	:   Optional[int]  				=     max_length
									lowercase_	:   Union[str, Any]  				=     2
									lowercase_	:   List[str]  				=     1
									lowercase_	:   str  				=     8
									lowercase_	:   int  				=     9

									for model_class in self.all_generative_model_classes:
																lowercase_	:   int  				=     model_class(snake_case__  )

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

																lowercase_	:   Dict  				=     jit(model.generate  )
																lowercase_	:   Optional[Any]  				=     jit_generate(snake_case__  ).sequences

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






		def 						snake_case__				(							self  )       -> Optional[int]:






									"""simple docstring"""
									lowercase_				,		lowercase_				,		lowercase_				,		lowercase_	:   Union[str, Any]  				=     self._get_input_ids_and_config()

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

									lowercase_	:   Optional[Any]  				=     False
									lowercase_	:   List[str]  				=     max_length

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

																lowercase_	:   Tuple  				=     model.generate(snake_case__,   attention_mask=snake_case__  ).sequences
																self.assertEqual(generation_outputs.shape[-1],   snake_case__  )

																lowercase_	:   Tuple  				=     jit(model.generate  )
																lowercase_	:   List[str]  				=     jit_generate(snake_case__,   attention_mask=snake_case__  ).sequences

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






		def 						snake_case__				(							self  )       -> Any:






									"""simple docstring"""
									lowercase_				,		lowercase_				,		lowercase_				,		lowercase_	:   int  				=     self._get_input_ids_and_config()

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

									lowercase_	:   Any  				=     True
									lowercase_	:   Union[str, Any]  				=     max_length

									for model_class in self.all_generative_model_classes:
																lowercase_	:   List[Any]  				=     model_class(snake_case__  )

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

																lowercase_	:   Optional[Any]  				=     jit(model.generate  )
																lowercase_	:   List[Any]  				=     jit_generate(snake_case__,   attention_mask=snake_case__  ).sequences

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






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






									"""simple docstring"""
									lowercase_				,		lowercase_				,		lowercase_				,		lowercase_	:   int  				=     self._get_input_ids_and_config()

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

									lowercase_	:   List[str]  				=     2
									lowercase_	:   Any  				=     max_length

									for model_class in self.all_generative_model_classes:
																lowercase_	:   int  				=     model_class(snake_case__  )

																lowercase_	:   Tuple  				=     model.generate(snake_case__,   attention_mask=snake_case__  ).sequences
																self.assertEqual(generation_outputs.shape[-1],   snake_case__  )

																lowercase_	:   List[str]  				=     jit(model.generate  )
																lowercase_	:   Tuple  				=     jit_generate(snake_case__,   attention_mask=snake_case__  ).sequences

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




@require_flax
class     UpperCamelCase__					(     unittest.TestCase				):






		'''simple docstring'''






		def 						snake_case__				(							self  )       -> Dict:






									"""simple docstring"""
									lowercase_	:   Union[str, Any]  				=     AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-bert"""  )
									lowercase_	:   Any  				=     FlaxAutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-bert-flax-only"""  )

									lowercase_	:   Dict  				=     """Hello world"""
									lowercase_	:   Union[str, Any]  				=     tokenizer(snake_case__,   return_tensors="""np"""  ).input_ids

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

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






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


UpperCAmelCase_							=				{
    """configuration_m2m_100""": ["""M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP""", """M2M100Config""", """M2M100OnnxConfig"""],
    """tokenization_m2m_100""": ["""M2M100Tokenizer"""],
}


try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase_							=				[
							    """M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """M2M100ForConditionalGeneration""",
							    """M2M100Model""",
							    """M2M100PreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_mam_aaa import M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP, MaMaaaConfig, MaMaaaOnnxConfig
							from .tokenization_mam_aaa import MaMaaaTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_mam_aaa import (
														    M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST,
														    MaMaaaForConditionalGeneration,
														    MaMaaaModel,
														    MaMaaaPreTrainedModel,
														)


else:
							import sys

							UpperCAmelCase_							=				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)