

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

					SCREAMING_SNAKE_CASE_        =      '0.12'  # assumed parallelism: 8


if is_torch_available():
					import torch

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  str  ,      lowerCAmelCase:  int  ,      lowerCAmelCase:  List[Any]=None )   ->       int:
				if rng is None:
								_UpperCAmelCase       :		Optional[Any] 		=    random.Random()

				_UpperCAmelCase       :		int 		=    1
				for dim in shape:
								total_dims *= dim

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

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

				return output

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Optional[int]  ,      lowerCAmelCase:  Optional[int]=None )   ->       Union[str, Any]:
				_UpperCAmelCase       :		str 		=    ids_tensor(lowerCAmelCase  ,      vocab_size=2  ,      rng=lowerCAmelCase )
				# make sure that at least one token is attended to for each batch
				_UpperCAmelCase       :		Optional[Any] 		=    1
				return attn_mask

@require_flax
class 				a  :
						_lowercase    						=			None
						_lowercase    						=			()


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase		,							_UpperCAmelCase       :		List[str] 		=    self.model_tester.prepare_config_and_inputs_for_common()

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

										_UpperCAmelCase       :		str 		=    jnp.ones_like(A_	)
										_UpperCAmelCase       :		Optional[int] 		=    attention_mask[:max_batch_size, :sequence_length]

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


						@is_pt_flax_cross_test
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase		,							_UpperCAmelCase		,							_UpperCAmelCase		,							_UpperCAmelCase       :		List[Any] 		=    self._get_input_ids_and_config()
										_UpperCAmelCase       :		Optional[int] 		=    False
										_UpperCAmelCase       :		Any 		=    max_length
										_UpperCAmelCase       :		str 		=    0

										for model_class in self.all_generative_model_classes:
														_UpperCAmelCase       :		Optional[Any] 		=    model_class(A_	)

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

														_UpperCAmelCase       :		Optional[Any] 		=    flax_model.generate(A_	).sequences
														_UpperCAmelCase       :		Optional[Any] 		=    pt_model.generate(torch.tensor(A_						,					dtype=torch.long	)	)

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

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


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase		,							_UpperCAmelCase		,							_UpperCAmelCase		,							_UpperCAmelCase       :		Any 		=    self._get_input_ids_and_config()
										_UpperCAmelCase       :		Dict 		=    False
										_UpperCAmelCase       :		Dict 		=    max_length

										for model_class in self.all_generative_model_classes:
														_UpperCAmelCase       :		Tuple 		=    model_class(A_	)

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

														_UpperCAmelCase       :		Tuple 		=    jit(model.generate	)
														_UpperCAmelCase       :		Tuple 		=    jit_generate(A_	).sequences

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


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase		,							_UpperCAmelCase		,							_UpperCAmelCase		,							_UpperCAmelCase       :		str 		=    self._get_input_ids_and_config()
										_UpperCAmelCase       :		str 		=    True
										_UpperCAmelCase       :		Any 		=    max_length

										for model_class in self.all_generative_model_classes:
														_UpperCAmelCase       :		Optional[int] 		=    model_class(A_	)

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

														_UpperCAmelCase       :		str 		=    jit(model.generate	)
														_UpperCAmelCase       :		int 		=    jit_generate(A_	).sequences

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


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase		,							_UpperCAmelCase		,							_UpperCAmelCase		,							_UpperCAmelCase       :		int 		=    self._get_input_ids_and_config()
										_UpperCAmelCase       :		Any 		=    False
										_UpperCAmelCase       :		Optional[Any] 		=    max_length
										_UpperCAmelCase       :		Any 		=    2

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

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

														_UpperCAmelCase       :		List[Any] 		=    jit(model.generate	)
														_UpperCAmelCase       :		List[str] 		=    jit_generate(A_	).sequences

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


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase		,							_UpperCAmelCase		,							_UpperCAmelCase		,							_UpperCAmelCase       :		Dict 		=    self._get_input_ids_and_config()
										_UpperCAmelCase       :		str 		=    False
										_UpperCAmelCase       :		Dict 		=    max_length
										_UpperCAmelCase       :		Dict 		=    2
										_UpperCAmelCase       :		int 		=    2

										for model_class in self.all_generative_model_classes:
														_UpperCAmelCase       :		Optional[int] 		=    model_class(A_	)

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


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


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

										for model_class in self.all_generative_model_classes:
														_UpperCAmelCase       :		Dict 		=    model_class(A_	)

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

														_UpperCAmelCase       :		List[Any] 		=    jit(model.generate	)
														_UpperCAmelCase       :		int 		=    jit_generate(A_	).sequences

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


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


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

										for model_class in self.all_generative_model_classes:
														_UpperCAmelCase       :		Dict 		=    model_class(A_	)

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

														_UpperCAmelCase       :		str 		=    jit(model.generate	)
														_UpperCAmelCase       :		Optional[int] 		=    jit_generate(A_	).sequences

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


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


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

										for model_class in self.all_generative_model_classes:
														_UpperCAmelCase       :		Any 		=    model_class(A_	)

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

														_UpperCAmelCase       :		Optional[int] 		=    jit(model.generate	)
														_UpperCAmelCase       :		int 		=    jit_generate(A_	).sequences

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


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase		,							_UpperCAmelCase		,							_UpperCAmelCase		,							_UpperCAmelCase       :		str 		=    self._get_input_ids_and_config()

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

										_UpperCAmelCase       :		Union[str, Any] 		=    False
										_UpperCAmelCase       :		Any 		=    max_length

										for model_class in self.all_generative_model_classes:
														_UpperCAmelCase       :		List[str] 		=    model_class(A_	)

														_UpperCAmelCase       :		Optional[Any] 		=    model.generate(A_						,					attention_mask=A_	).sequences
														self.assertEqual(generation_outputs.shape[-1]						,					A_	)

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

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


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase		,							_UpperCAmelCase		,							_UpperCAmelCase		,							_UpperCAmelCase       :		Dict 		=    self._get_input_ids_and_config()

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

										_UpperCAmelCase       :		Dict 		=    True
										_UpperCAmelCase       :		int 		=    max_length

										for model_class in self.all_generative_model_classes:
														_UpperCAmelCase       :		Optional[int] 		=    model_class(A_	)

														_UpperCAmelCase       :		Optional[int] 		=    model.generate(A_						,					attention_mask=A_	).sequences
														self.assertEqual(generation_outputs.shape[-1]						,					A_	)

														_UpperCAmelCase       :		int 		=    jit(model.generate	)
														_UpperCAmelCase       :		Optional[int] 		=    jit_generate(A_						,					attention_mask=A_	).sequences

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





						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


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

										# pad attention mask on the left
										_UpperCAmelCase       :		Optional[int] 		=    attention_mask.at[(0, 0)].set(0	)

										_UpperCAmelCase       :		Optional[int] 		=    2
										_UpperCAmelCase       :		Tuple 		=    max_length

										for model_class in self.all_generative_model_classes:
														_UpperCAmelCase       :		int 		=    model_class(A_	)

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

														_UpperCAmelCase       :		Optional[int] 		=    jit(model.generate	)
														_UpperCAmelCase       :		Optional[int] 		=    jit_generate(A_						,					attention_mask=A_	).sequences

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







@require_flax
class 				a  (	unittest.TestCase				):


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


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

										_UpperCAmelCase       :		List[Any] 		=    "Hello world"
										_UpperCAmelCase       :		Optional[Any] 		=    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"	):
														_UpperCAmelCase       :		Dict 		=    {"foo": "bar"}
														model.generate(A_						,					**A_	)





import warnings

from ...utils import logging
from .image_processing_deformable_detr import DeformableDetrImageProcessor


SCREAMING_SNAKE_CASE_        =      logging.get_logger(__name__)

class 				a  (	UpperCAmelCase				):


						def __init__(       self						,					*A_						,					**A_	):







										'''simple docstring'''


										warnings.warn(
										    "The class DeformableDetrFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
										    " Please use DeformableDetrImageProcessor instead."						,					A_						,					)
										super().__init__(*A_						,					**A_	)


