
import unittest

import numpy as np

from transformers import is_flax_available
from transformers.testing_utils import require_flax

from ..test_modeling_flax_common import ids_tensor


if is_flax_available():
				import jax
				import jax.numpy as jnp

				from transformers.generation import (
				    FlaxForcedBOSTokenLogitsProcessor,
				    FlaxForcedEOSTokenLogitsProcessor,
				    FlaxLogitsProcessorList,
				    FlaxMinLengthLogitsProcessor,
				    FlaxTemperatureLogitsWarper,
				    FlaxTopKLogitsWarper,
				    FlaxTopPLogitsWarper,
				)





@require_flax
class       _snake_case					(     unittest.TestCase				):







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a		):
				__magic_name__		:		List[str]	   =     jnp.ones((batch_size, length)		) / length
				return scores







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Optional[Any]	   =     None
				__magic_name__		:		Tuple	   =     20

				__magic_name__		:		Optional[int]	   =     self._get_uniform_logits(batch_size=2						,   length=_a		)

				# tweak scores to not be uniform anymore
				__magic_name__		:		Optional[int]	   =     scores.at[1, 5].set((1 / length) + 0.1		)  # peak, 1st batch
				__magic_name__		:		Any	   =     scores.at[1, 10].set((1 / length) - 0.4		)  # valley, 1st batch

				# compute softmax
				__magic_name__		:		Optional[int]	   =     jax.nn.softmax(_a						,   axis=-1		)

				__magic_name__		:		List[str]	   =     FlaxTemperatureLogitsWarper(temperature=0.5		)
				__magic_name__		:		List[Any]	   =     FlaxTemperatureLogitsWarper(temperature=1.3		)

				__magic_name__		:		List[str]	   =     jax.nn.softmax(temp_dist_warper_sharper(_a						,   scores.copy()						,   cur_len=_a		)						,   axis=-1		)
				__magic_name__		:		Optional[Any]	   =     jax.nn.softmax(temp_dist_warper_smoother(_a						,   scores.copy()						,   cur_len=_a		)						,   axis=-1		)

				# uniform distribution stays uniform
				self.assertTrue(jnp.allclose(probs[0, :]						,   warped_prob_sharp[0, :]						,   atol=1e-3		)		)
				self.assertTrue(jnp.allclose(probs[0, :]						,   warped_prob_smooth[0, :]						,   atol=1e-3		)		)

				# sharp peaks get higher, valleys get lower
				self.assertLess(probs[1, :].max()						,   warped_prob_sharp[1, :].max()		)
				self.assertGreater(probs[1, :].min()						,   warped_prob_sharp[1, :].min()		)

				# smooth peaks get lower, valleys get higher
				self.assertGreater(probs[1, :].max()						,   warped_prob_smooth[1, :].max()		)
				self.assertLess(probs[1, :].min()						,   warped_prob_smooth[1, :].min()		)







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Union[str, Any]	   =     None
				__magic_name__		:		Optional[Any]	   =     10
				__magic_name__		:		Union[str, Any]	   =     2

				# create ramp distribution
				__magic_name__		:		List[Any]	   =     np.broadcast_to(np.arange(_a		)[None, :]						,   (batch_size, vocab_size)		).copy()
				__magic_name__		:		Optional[Any]	   =     ramp_logits[1:, : vocab_size // 2] + vocab_size

				__magic_name__		:		List[Any]	   =     FlaxTopKLogitsWarper(3		)

				__magic_name__		:		Optional[Any]	   =     top_k_warp(_a						,   _a						,   cur_len=_a		)

				# check that correct tokens are filtered
				self.assertListEqual(jnp.isinf(scores[0]		).tolist()						,   7 * [True] + 3 * [False]		)
				self.assertListEqual(jnp.isinf(scores[1]		).tolist()						,   2 * [True] + 3 * [False] + 5 * [True]		)

				# check special case
				__magic_name__		:		Dict	   =     5
				__magic_name__		:		Optional[Any]	   =     FlaxTopKLogitsWarper(top_k=1						,   filter_value=0.0						,   min_tokens_to_keep=3		)

				__magic_name__		:		Dict	   =     np.broadcast_to(np.arange(_a		)[None, :]						,   (batch_size, length)		).copy()
				__magic_name__		:		str	   =     top_k_warp_safety_check(_a						,   _a						,   cur_len=_a		)

				# min_tokens overwrites k: 3 tokens are kept => 2 tokens are nullified
				self.assertListEqual((scores == 0.0).sum(axis=-1		).tolist()						,   [2, 2]		)







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		List[Any]	   =     None
				__magic_name__		:		Optional[Any]	   =     10
				__magic_name__		:		Optional[Any]	   =     2

				# create distribution and take log (inverse to Softmax as taken in TopPLogitsWarper)
				__magic_name__		:		List[str]	   =     np.log(np.array([[0.3, 0.1, 0.1, 0.5], [0.15, 0.3, 0.3, 0.25]]		)		)

				__magic_name__		:		Optional[Any]	   =     FlaxTopPLogitsWarper(0.8		)
				__magic_name__		:		Dict	   =     np.exp(top_p_warp(_a						,   _a						,   cur_len=_a		)		)

				# dist should be filtered to keep min num values so that sum is >= top_p
				# exp (-inf) => 0
				__magic_name__		:		int	   =     np.array([[0.3, 0.0, 0.0, 0.5], [0.0, 0.3, 0.3, 0.25]]		)
				self.assertTrue(np.allclose(_a						,   _a						,   atol=1e-3		)		)

				# check edge cases with negative and extreme logits
				__magic_name__		:		Optional[Any]	   =     np.broadcast_to(np.arange(_a		)[None, :]						,   (batch_size, vocab_size)		).copy() - (
				    vocab_size // 2
				)

				# make ramp_logits more extreme
				__magic_name__		:		Optional[Any]	   =     ramp_logits[1] * 1_00.0

				# make sure at least 2 tokens are kept
				__magic_name__		:		Union[str, Any]	   =     FlaxTopPLogitsWarper(0.9						,   min_tokens_to_keep=2						,   filter_value=0.0		)
				__magic_name__		:		str	   =     top_p_warp(_a						,   _a						,   cur_len=_a		)

				# first batch should keep three tokens, second batch would keep only 1, but due to `min_tokens_to_keep=2` keeps 2.
				self.assertListEqual((filtered_dist != 0.0).sum(axis=-1		).tolist()						,   [3, 2]		)







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Any	   =     20
				__magic_name__		:		List[Any]	   =     4
				__magic_name__		:		Dict	   =     0

				__magic_name__		:		Dict	   =     FlaxMinLengthLogitsProcessor(min_length=10						,   eos_token_id=_a		)

				# check that min length is applied at length 5
				__magic_name__		:		Union[str, Any]	   =     ids_tensor((batch_size, 20)						,   vocab_size=20		)
				__magic_name__		:		Any	   =     5
				__magic_name__		:		Tuple	   =     self._get_uniform_logits(_a						,   _a		)
				__magic_name__		:		Union[str, Any]	   =     min_dist_processor(_a						,   _a						,   cur_len=_a		)
				self.assertListEqual(scores_before_min_length[:, eos_token_id].tolist()						,   4 * [-float("inf"		)]		)

				# check that min length is not applied anymore at length 15
				__magic_name__		:		Dict	   =     self._get_uniform_logits(_a						,   _a		)
				__magic_name__		:		List[str]	   =     15
				__magic_name__		:		str	   =     min_dist_processor(_a						,   _a						,   cur_len=_a		)
				self.assertFalse(jnp.isinf(_a		).any()		)







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Union[str, Any]	   =     20
				__magic_name__		:		Any	   =     4
				__magic_name__		:		Union[str, Any]	   =     0

				__magic_name__		:		Tuple	   =     FlaxForcedBOSTokenLogitsProcessor(bos_token_id=_a		)

				# check that all scores are -inf except the bos_token_id score
				__magic_name__		:		Optional[Any]	   =     ids_tensor((batch_size, 1)						,   vocab_size=20		)
				__magic_name__		:		Optional[Any]	   =     1
				__magic_name__		:		List[str]	   =     self._get_uniform_logits(_a						,   _a		)
				__magic_name__		:		Optional[int]	   =     logits_processor(_a						,   _a						,   cur_len=_a		)
				self.assertTrue(jnp.isneginf(scores[:, bos_token_id + 1 :]		).all()		)
				self.assertListEqual(scores[:, bos_token_id].tolist()						,   4 * [0]		)  # score for bos_token_id shold be zero

				# check that bos_token_id is not forced if current length is greater than 1
				__magic_name__		:		Optional[int]	   =     3
				__magic_name__		:		List[Any]	   =     self._get_uniform_logits(_a						,   _a		)
				__magic_name__		:		List[Any]	   =     logits_processor(_a						,   _a						,   cur_len=_a		)
				self.assertFalse(jnp.isinf(_a		).any()		)







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		List[str]	   =     20
				__magic_name__		:		Optional[Any]	   =     4
				__magic_name__		:		Any	   =     0
				__magic_name__		:		Optional[Any]	   =     5

				__magic_name__		:		Dict	   =     FlaxForcedEOSTokenLogitsProcessor(max_length=_a						,   eos_token_id=_a		)

				# check that all scores are -inf except the eos_token_id when max_length is reached
				__magic_name__		:		Optional[int]	   =     ids_tensor((batch_size, 4)						,   vocab_size=20		)
				__magic_name__		:		List[Any]	   =     4
				__magic_name__		:		Optional[int]	   =     self._get_uniform_logits(_a						,   _a		)
				__magic_name__		:		List[Any]	   =     logits_processor(_a						,   _a						,   cur_len=_a		)
				self.assertTrue(jnp.isneginf(scores[:, eos_token_id + 1 :]		).all()		)
				self.assertListEqual(scores[:, eos_token_id].tolist()						,   4 * [0]		)  # score for eos_token_id should be zero

				# check that eos_token_id is not forced if max_length is not reached
				__magic_name__		:		List[str]	   =     3
				__magic_name__		:		Union[str, Any]	   =     self._get_uniform_logits(_a						,   _a		)
				__magic_name__		:		List[Any]	   =     logits_processor(_a						,   _a						,   cur_len=_a		)
				self.assertFalse(jnp.isinf(_a		).any()		)







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Optional[int]	   =     4
				__magic_name__		:		Optional[int]	   =     10
				__magic_name__		:		str	   =     15
				__magic_name__		:		Tuple	   =     2
				__magic_name__		:		int	   =     1
				__magic_name__		:		Dict	   =     15

				# dummy input_ids and scores
				__magic_name__		:		Optional[Any]	   =     ids_tensor((batch_size, sequence_length)						,   _a		)
				__magic_name__		:		Optional[Any]	   =     input_ids.copy()

				__magic_name__		:		Tuple	   =     self._get_uniform_logits(_a						,   _a		)
				__magic_name__		:		Dict	   =     scores.copy()

				# instantiate all dist processors
				__magic_name__		:		Union[str, Any]	   =     FlaxTemperatureLogitsWarper(temperature=0.5		)
				__magic_name__		:		int	   =     FlaxTopKLogitsWarper(3		)
				__magic_name__		:		List[Any]	   =     FlaxTopPLogitsWarper(0.8		)

				# instantiate all logits processors
				__magic_name__		:		str	   =     FlaxMinLengthLogitsProcessor(min_length=10						,   eos_token_id=_a		)
				__magic_name__		:		Any	   =     FlaxForcedBOSTokenLogitsProcessor(bos_token_id=_a		)
				__magic_name__		:		Optional[int]	   =     FlaxForcedEOSTokenLogitsProcessor(max_length=_a						,   eos_token_id=_a		)

				__magic_name__		:		Optional[int]	   =     10

				# no processor list
				__magic_name__		:		List[str]	   =     temp_dist_warp(_a						,   _a						,   cur_len=_a		)
				__magic_name__		:		str	   =     top_k_warp(_a						,   _a						,   cur_len=_a		)
				__magic_name__		:		Tuple	   =     top_p_warp(_a						,   _a						,   cur_len=_a		)
				__magic_name__		:		str	   =     min_dist_proc(_a						,   _a						,   cur_len=_a		)
				__magic_name__		:		Tuple	   =     bos_dist_proc(_a						,   _a						,   cur_len=_a		)
				__magic_name__		:		str	   =     eos_dist_proc(_a						,   _a						,   cur_len=_a		)

				# with processor list
				__magic_name__		:		List[str]	   =     FlaxLogitsProcessorList(
				    [temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc]		)
				__magic_name__		:		Tuple	   =     processor(_a						,   _a						,   cur_len=_a		)

				# scores should be equal
				self.assertTrue(jnp.allclose(_a						,   _a						,   atol=1e-3		)		)

				# input_ids should never be changed
				self.assertListEqual(input_ids.tolist()						,   input_ids_comp.tolist()		)







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		int	   =     4
				__magic_name__		:		Dict	   =     10
				__magic_name__		:		str	   =     15
				__magic_name__		:		List[Any]	   =     2
				__magic_name__		:		List[Any]	   =     1
				__magic_name__		:		Dict	   =     15

				# dummy input_ids and scores
				__magic_name__		:		List[str]	   =     ids_tensor((batch_size, sequence_length)						,   _a		)
				__magic_name__		:		int	   =     input_ids.copy()

				__magic_name__		:		Optional[int]	   =     self._get_uniform_logits(_a						,   _a		)
				__magic_name__		:		Optional[int]	   =     scores.copy()

				# instantiate all dist processors
				__magic_name__		:		Dict	   =     FlaxTemperatureLogitsWarper(temperature=0.5		)
				__magic_name__		:		Optional[int]	   =     FlaxTopKLogitsWarper(3		)
				__magic_name__		:		Dict	   =     FlaxTopPLogitsWarper(0.8		)

				# instantiate all logits processors
				__magic_name__		:		Optional[Any]	   =     FlaxMinLengthLogitsProcessor(min_length=10						,   eos_token_id=_a		)
				__magic_name__		:		Optional[int]	   =     FlaxForcedBOSTokenLogitsProcessor(bos_token_id=_a		)
				__magic_name__		:		Union[str, Any]	   =     FlaxForcedEOSTokenLogitsProcessor(max_length=_a						,   eos_token_id=_a		)

				__magic_name__		:		List[Any]	   =     10

				# no processor list
				def run_no_processor_list(_a						,   _a						,   _a		):
					__magic_name__		:		List[str]	   =     temp_dist_warp(_a						,   _a						,   cur_len=_a		)
					__magic_name__		:		int	   =     top_k_warp(_a						,   _a						,   cur_len=_a		)
					__magic_name__		:		Any	   =     top_p_warp(_a						,   _a						,   cur_len=_a		)
					__magic_name__		:		str	   =     min_dist_proc(_a						,   _a						,   cur_len=_a		)
					__magic_name__		:		List[str]	   =     bos_dist_proc(_a						,   _a						,   cur_len=_a		)
					__magic_name__		:		Optional[int]	   =     eos_dist_proc(_a						,   _a						,   cur_len=_a		)
					return scores

				# with processor list
				def run_processor_list(_a						,   _a						,   _a		):
					__magic_name__		:		Optional[int]	   =     FlaxLogitsProcessorList(
					    [temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc]		)
					__magic_name__		:		int	   =     processor(_a						,   _a						,   cur_len=_a		)
					return scores

				__magic_name__		:		Optional[int]	   =     jax.jit(_a		)
				__magic_name__		:		List[str]	   =     jax.jit(_a		)

				__magic_name__		:		Optional[Any]	   =     jitted_run_no_processor_list(_a						,   _a						,   _a		)
				__magic_name__		:		Tuple	   =     jitted_run_processor_list(_a						,   _a						,   _a		)

				# scores should be equal
				self.assertTrue(jnp.allclose(_a						,   _a						,   atol=1e-3		)		)

				# input_ids should never be changed
				self.assertListEqual(input_ids.tolist()						,   input_ids_comp.tolist()		)


from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


snake_case :		Dict							      =  logging.get_logger(__name__)

snake_case :		List[Any]							      =  {
    "YituTech/conv-bert-base": "https://huggingface.co/YituTech/conv-bert-base/resolve/main/config.json",
    "YituTech/conv-bert-medium-small": (
        "https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/config.json"
    ),
    "YituTech/conv-bert-small": "https://huggingface.co/YituTech/conv-bert-small/resolve/main/config.json",
    # See all ConvBERT models at https://huggingface.co/models?filter=convbert
}





class       _snake_case					(     snake_case				):
			UpperCamelCase__          =    'convbert'







			def __init__(				self						,   _a=30_522						,   _a=768						,   _a=12						,   _a=12						,   _a=3_072						,   _a="gelu"						,   _a=0.1						,   _a=0.1						,   _a=512						,   _a=2						,   _a=0.02						,   _a=1e-12						,   _a=1						,   _a=0						,   _a=2						,   _a=768						,   _a=2						,   _a=9						,   _a=1						,   _a=None						,   **_a						,   ):
				super().__init__(
				    pad_token_id=_a						,   bos_token_id=_a						,   eos_token_id=_a						,   **_a						,   )

				__magic_name__		:		Tuple	   =     vocab_size
				__magic_name__		:		List[Any]	   =     hidden_size
				__magic_name__		:		Union[str, Any]	   =     num_hidden_layers
				__magic_name__		:		List[Any]	   =     num_attention_heads
				__magic_name__		:		str	   =     intermediate_size
				__magic_name__		:		Any	   =     hidden_act
				__magic_name__		:		List[Any]	   =     hidden_dropout_prob
				__magic_name__		:		Optional[int]	   =     attention_probs_dropout_prob
				__magic_name__		:		Tuple	   =     max_position_embeddings
				__magic_name__		:		str	   =     type_vocab_size
				__magic_name__		:		List[str]	   =     initializer_range
				__magic_name__		:		Tuple	   =     layer_norm_eps
				__magic_name__		:		List[Any]	   =     embedding_size
				__magic_name__		:		List[Any]	   =     head_ratio
				__magic_name__		:		str	   =     conv_kernel_size
				__magic_name__		:		Dict	   =     num_groups
				__magic_name__		:		str	   =     classifier_dropout







class       _snake_case					(     snake_case				):







			@property
			def 			SCREAMING_SNAKE_CASE    (				self		):
				if self.task == "multiple-choice":
					__magic_name__		:		Dict	   =     {0: "batch", 1: "choice", 2: "sequence"}
				else:
					__magic_name__		:		Dict	   =     {0: "batch", 1: "sequence"}
				return OrderedDict(
				    [
				        ("input_ids", dynamic_axis),
				        ("attention_mask", dynamic_axis),
				        ("token_type_ids", dynamic_axis),
				    ]		)
