
import numpy as np

from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch

from .test_feature_extraction_common import FeatureExtractionSavingTestMixin







class 	lowercase    (				UpperCamelCase__							):


		"""simple docstring"""




		a__					       =  None
		a__					       =  None


		@property
		def 		A__					(				self):
			return self.feat_extract_tester.prepare_feat_extract_dict()


		def 		A__					(				self):
			_UpperCamelCase   :		Any			=						self.feature_extraction_class(**self.feat_extract_dict)
			self.assertTrue(hasattr(__snake_case	,       'feature_size'))
			self.assertTrue(hasattr(__snake_case	,       'sampling_rate'))
			self.assertTrue(hasattr(__snake_case	,       'padding_value'))


		def 		A__					(				self):
			_UpperCamelCase   :		str			=						self.feat_extract_tester.prepare_inputs_for_common()
			_UpperCamelCase   :		List[Any]			=						self.feature_extraction_class(**self.feat_extract_dict)
			_UpperCamelCase   :		Any			=						feat_extract.model_input_names[0]

			_UpperCamelCase   :		Dict			=						BatchFeature({input_name: speech_inputs})

			self.assertTrue(all(len(__snake_case) == len(__snake_case) for x, y in zip(__snake_case	,       processed_features[input_name])))

			_UpperCamelCase   :		Any			=						self.feat_extract_tester.prepare_inputs_for_common(equal_length=__snake_case)
			_UpperCamelCase   :		Optional[int]			=						BatchFeature({input_name: speech_inputs}	,       tensor_type='np')

			_UpperCamelCase   :		List[Any]			=						processed_features[input_name]

			if len(batch_features_input.shape) < 3:
				_UpperCamelCase   :		Tuple			=						batch_features_input[:, :, None]

			self.assertTrue(
			    batch_features_input.shape
			    == (self.feat_extract_tester.batch_size, len(speech_inputs[0]), self.feat_extract_tester.feature_size))


		@require_torch
		def 		A__					(				self):
			_UpperCamelCase   :		Optional[int]			=						self.feat_extract_tester.prepare_inputs_for_common(equal_length=__snake_case)
			_UpperCamelCase   :		Dict			=						self.feature_extraction_class(**self.feat_extract_dict)
			_UpperCamelCase   :		Optional[int]			=						feat_extract.model_input_names[0]

			_UpperCamelCase   :		Dict			=						BatchFeature({input_name: speech_inputs}	,       tensor_type='pt')

			_UpperCamelCase   :		List[Any]			=						processed_features[input_name]

			if len(batch_features_input.shape) < 3:
				_UpperCamelCase   :		int			=						batch_features_input[:, :, None]

			self.assertTrue(
			    batch_features_input.shape
			    == (self.feat_extract_tester.batch_size, len(speech_inputs[0]), self.feat_extract_tester.feature_size))


		@require_tf
		def 		A__					(				self):
			_UpperCamelCase   :		Dict			=						self.feat_extract_tester.prepare_inputs_for_common(equal_length=__snake_case)
			_UpperCamelCase   :		List[str]			=						self.feature_extraction_class(**self.feat_extract_dict)
			_UpperCamelCase   :		str			=						feat_extract.model_input_names[0]

			_UpperCamelCase   :		str			=						BatchFeature({input_name: speech_inputs}	,       tensor_type='tf')

			_UpperCamelCase   :		Tuple			=						processed_features[input_name]

			if len(batch_features_input.shape) < 3:
				_UpperCamelCase   :		int			=						batch_features_input[:, :, None]

			self.assertTrue(
			    batch_features_input.shape
			    == (self.feat_extract_tester.batch_size, len(speech_inputs[0]), self.feat_extract_tester.feature_size))


		def 		A__					(				self	,       __snake_case=False):
			def _inputs_have_equal_length(__snake_case):
				_UpperCamelCase   :		Dict			=						len(input[0])
				for input_slice in input[1:]:
					if len(__snake_case) != length:
						return False
				return True

			def _inputs_are_equal(__snake_case	,       __snake_case):
				if len(__snake_case) != len(__snake_case):
					return False

				for input_slice_a, input_slice_a in zip(__snake_case	,       __snake_case):
					if not np.allclose(np.asarray(__snake_case)	,       np.asarray(__snake_case)	,       atol=1e-3):
						return False
				return True

			_UpperCamelCase   :		Union[str, Any]			=						self.feature_extraction_class(**self.feat_extract_dict)
			_UpperCamelCase   :		int			=						self.feat_extract_tester.prepare_inputs_for_common(numpify=__snake_case)
			_UpperCamelCase   :		Any			=						feat_extract.model_input_names[0]

			_UpperCamelCase   :		str			=						BatchFeature({input_name: speech_inputs})

			_UpperCamelCase   :		Optional[int]			=						self.feat_extract_tester.seq_length_diff
			_UpperCamelCase   :		Union[str, Any]			=						self.feat_extract_tester.max_seq_length + pad_diff
			_UpperCamelCase   :		Union[str, Any]			=						self.feat_extract_tester.min_seq_length
			_UpperCamelCase   :		Dict			=						self.feat_extract_tester.batch_size
			_UpperCamelCase   :		Union[str, Any]			=						self.feat_extract_tester.feature_size

			# test padding for List[int] + numpy
			_UpperCamelCase   :		int			=						feat_extract.pad(__snake_case	,       padding=__snake_case)
			_UpperCamelCase   :		List[str]			=						input_a[input_name]

			_UpperCamelCase   :		Optional[int]			=						feat_extract.pad(__snake_case	,       padding='longest')
			_UpperCamelCase   :		List[Any]			=						input_a[input_name]

			_UpperCamelCase   :		List[Any]			=						feat_extract.pad(__snake_case	,       padding='max_length'	,       max_length=len(speech_inputs[-1]))
			_UpperCamelCase   :		int			=						input_a[input_name]

			_UpperCamelCase   :		str			=						feat_extract.pad(__snake_case	,       padding='longest'	,       return_tensors='np')
			_UpperCamelCase   :		Optional[int]			=						input_a[input_name]

			# max_length parameter has to be provided when setting `padding="max_length"`
			with self.assertRaises(__snake_case):
				feat_extract.pad(__snake_case	,       padding='max_length')[input_name]

			_UpperCamelCase   :		int			=						feat_extract.pad(
			    __snake_case	,       padding='max_length'	,       max_length=__snake_case	,       return_tensors='np')
			_UpperCamelCase   :		Dict			=						input_a[input_name]

			self.assertFalse(_inputs_have_equal_length(__snake_case))
			self.assertTrue(_inputs_have_equal_length(__snake_case))
			self.assertTrue(_inputs_have_equal_length(__snake_case))
			self.assertTrue(_inputs_are_equal(__snake_case	,       __snake_case))
			self.assertTrue(len(input_a[0]) == pad_min_length)
			self.assertTrue(len(input_a[1]) == pad_min_length + pad_diff)
			self.assertTrue(input_a.shape[:2] == (batch_size, len(input_a[0])))
			self.assertTrue(input_a.shape[:2] == (batch_size, pad_max_length))

			if feature_size > 1:
				self.assertTrue(input_a.shape[2] == input_a.shape[2] == feature_size)

			# test padding for `pad_to_multiple_of` for List[int] + numpy
			_UpperCamelCase   :		Dict			=						feat_extract.pad(__snake_case	,       pad_to_multiple_of=10)
			_UpperCamelCase   :		Dict			=						input_a[input_name]

			_UpperCamelCase   :		Optional[Any]			=						feat_extract.pad(__snake_case	,       padding='longest'	,       pad_to_multiple_of=10)
			_UpperCamelCase   :		Any			=						input_a[input_name]

			_UpperCamelCase   :		List[Any]			=						feat_extract.pad(
			    __snake_case	,       padding='max_length'	,       pad_to_multiple_of=10	,       max_length=__snake_case)
			_UpperCamelCase   :		int			=						input_a[input_name]

			_UpperCamelCase   :		int			=						feat_extract.pad(
			    __snake_case	,       padding='max_length'	,       pad_to_multiple_of=10	,       max_length=__snake_case	,       return_tensors='np'	,       )
			_UpperCamelCase   :		List[str]			=						input_a[input_name]

			self.assertTrue(all(len(__snake_case) % 10 == 0 for x in input_a))
			self.assertTrue(_inputs_are_equal(__snake_case	,       __snake_case))

			_UpperCamelCase   :		Dict			=						pad_max_length if pad_max_length % 10 == 0 else (pad_max_length // 10 + 1) * 10
			self.assertTrue(all(len(__snake_case) == expected_mult_pad_length for x in input_a))
			self.assertEqual(input_a.shape[:2]	,       (batch_size, expected_mult_pad_length))

			if feature_size > 1:
				self.assertTrue(input_a.shape[2] == feature_size)

			# Check padding value is correct
			_UpperCamelCase   :		int			=						(np.ones(self.feat_extract_tester.feature_size) * feat_extract.padding_value).sum()
			self.assertTrue(
			    abs(np.asarray(input_a[0])[pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length))
			    < 1e-3)
			self.assertTrue(
			    abs(
			        np.asarray(input_a[1])[pad_min_length + pad_diff :].sum()
			        - padding_vector_sum * (pad_max_length - pad_min_length - pad_diff))
			    < 1e-3)
			self.assertTrue(
			    abs(
			        np.asarray(input_a[2])[pad_min_length + 2 * pad_diff :].sum()
			        - padding_vector_sum * (pad_max_length - pad_min_length - 2 * pad_diff))
			    < 1e-3)
			self.assertTrue(
			    abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length)) < 1e-3)
			self.assertTrue(
			    abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (expected_mult_pad_length - pad_min_length))
			    < 1e-3)


		def 		A__					(				self	,       __snake_case=False):
			def _inputs_have_equal_length(__snake_case):
				_UpperCamelCase   :		int			=						len(input[0])
				for input_slice in input[1:]:
					if len(__snake_case) != length:
						return False
				return True

			def _inputs_are_equal(__snake_case	,       __snake_case):
				if len(__snake_case) != len(__snake_case):
					return False

				for input_slice_a, input_slice_a in zip(__snake_case	,       __snake_case):
					if not np.allclose(np.asarray(__snake_case)	,       np.asarray(__snake_case)	,       atol=1e-3):
						return False
				return True

			_UpperCamelCase   :		str			=						self.feature_extraction_class(**self.feat_extract_dict)
			_UpperCamelCase   :		Any			=						self.feat_extract_tester.prepare_inputs_for_common(numpify=__snake_case)
			_UpperCamelCase   :		Tuple			=						feat_extract.model_input_names[0]

			_UpperCamelCase   :		Dict			=						BatchFeature({input_name: speech_inputs})

			# truncate to smallest
			_UpperCamelCase   :		str			=						feat_extract.pad(
			    __snake_case	,       padding='max_length'	,       max_length=len(speech_inputs[0])	,       truncation=__snake_case)
			_UpperCamelCase   :		int			=						input_a[input_name]

			_UpperCamelCase   :		Optional[int]			=						feat_extract.pad(__snake_case	,       padding='max_length'	,       max_length=len(speech_inputs[0]))
			_UpperCamelCase   :		Union[str, Any]			=						input_a[input_name]

			self.assertTrue(_inputs_have_equal_length(__snake_case))
			self.assertFalse(_inputs_have_equal_length(__snake_case))

			# truncate to smallest with np
			_UpperCamelCase   :		List[Any]			=						feat_extract.pad(
			    __snake_case	,       padding='max_length'	,       max_length=len(speech_inputs[0])	,       return_tensors='np'	,       truncation=__snake_case	,       )
			_UpperCamelCase   :		Optional[int]			=						input_a[input_name]

			_UpperCamelCase   :		Union[str, Any]			=						feat_extract.pad(
			    __snake_case	,       padding='max_length'	,       max_length=len(speech_inputs[0])	,       return_tensors='np')
			_UpperCamelCase   :		str			=						input_a[input_name]

			self.assertTrue(_inputs_have_equal_length(__snake_case))
			self.assertTrue(input_a.shape[1] == len(speech_inputs[0]))

			# since truncation forces padding to be smaller than longest input
			# function can't return `np.ndarray`, but has to return list
			self.assertFalse(_inputs_have_equal_length(__snake_case))

			# truncate to middle
			_UpperCamelCase   :		Union[str, Any]			=						feat_extract.pad(
			    __snake_case	,       padding='max_length'	,       max_length=len(speech_inputs[1])	,       truncation=__snake_case	,       return_tensors='np'	,       )
			_UpperCamelCase   :		Tuple			=						input_a[input_name]

			_UpperCamelCase   :		Union[str, Any]			=						feat_extract.pad(
			    __snake_case	,       padding='max_length'	,       max_length=len(speech_inputs[1])	,       truncation=__snake_case)
			_UpperCamelCase   :		Dict			=						input_a[input_name]

			_UpperCamelCase   :		Optional[Any]			=						feat_extract.pad(
			    __snake_case	,       padding='max_length'	,       max_length=len(speech_inputs[1])	,       return_tensors='np')
			_UpperCamelCase   :		Tuple			=						input_a[input_name]

			self.assertTrue(input_a.shape[1] == len(speech_inputs[1]))
			self.assertTrue(_inputs_have_equal_length(__snake_case))
			self.assertTrue(_inputs_have_equal_length(__snake_case))
			self.assertTrue(_inputs_are_equal(__snake_case	,       __snake_case))

			# since truncation forces padding to be smaller than longest input
			# function can't return `np.ndarray`, but has to return list
			self.assertFalse(_inputs_have_equal_length(__snake_case))
			self.assertTrue(len(input_a[-1]) == len(speech_inputs[-1]))

			# padding has to be max_length when setting `truncation=True`
			with self.assertRaises(__snake_case):
				feat_extract.pad(__snake_case	,       truncation=__snake_case)[input_name]

			# padding has to be max_length when setting `truncation=True`
			with self.assertRaises(__snake_case):
				feat_extract.pad(__snake_case	,       padding='longest'	,       truncation=__snake_case)[input_name]

			# padding has to be max_length when setting `truncation=True`
			with self.assertRaises(__snake_case):
				feat_extract.pad(__snake_case	,       padding='longest'	,       truncation=__snake_case)[input_name]

			# max_length parameter has to be provided when setting `truncation=True` and padding="max_length"
			with self.assertRaises(__snake_case):
				feat_extract.pad(__snake_case	,       padding='max_length'	,       truncation=__snake_case)[input_name]

			# test truncation for `pad_to_multiple_of` for List[int] + numpy
			_UpperCamelCase   :		Any			=						12
			_UpperCamelCase   :		Optional[int]			=						feat_extract.pad(
			    __snake_case	,       padding='max_length'	,       max_length=len(speech_inputs[0])	,       pad_to_multiple_of=__snake_case	,       truncation=__snake_case	,       )
			_UpperCamelCase   :		Optional[int]			=						input_a[input_name]

			_UpperCamelCase   :		Any			=						feat_extract.pad(
			    __snake_case	,       padding='max_length'	,       max_length=len(speech_inputs[0])	,       pad_to_multiple_of=__snake_case	,       )
			_UpperCamelCase   :		int			=						input_a[input_name]

			# retrieve expected_length as multiple of pad_to_multiple_of
			_UpperCamelCase   :		Union[str, Any]			=						len(speech_inputs[0])
			if expected_length % pad_to_multiple_of != 0:
				_UpperCamelCase   :		Dict			=						((len(speech_inputs[0]) // pad_to_multiple_of) + 1) * pad_to_multiple_of

			self.assertTrue(len(input_a[0]) == expected_length)
			self.assertTrue(_inputs_have_equal_length(__snake_case))
			self.assertFalse(_inputs_have_equal_length(__snake_case))


		def 		A__					(				self):
			self._check_padding(numpify=__snake_case)


		def 		A__					(				self):
			self._check_padding(numpify=__snake_case)


		def 		A__					(				self):
			self._check_truncation(numpify=__snake_case)


		def 		A__					(				self):
			self._check_truncation(numpify=__snake_case)


		@require_torch
		def 		A__					(				self):
			_UpperCamelCase   :		Optional[Any]			=						self.feature_extraction_class(**self.feat_extract_dict)
			_UpperCamelCase   :		Union[str, Any]			=						self.feat_extract_tester.prepare_inputs_for_common()
			_UpperCamelCase   :		List[Any]			=						feat_extract.model_input_names[0]

			_UpperCamelCase   :		Union[str, Any]			=						BatchFeature({input_name: speech_inputs})

			_UpperCamelCase   :		str			=						feat_extract.pad(__snake_case	,       padding='longest'	,       return_tensors='np')[input_name]
			_UpperCamelCase   :		str			=						feat_extract.pad(__snake_case	,       padding='longest'	,       return_tensors='pt')[input_name]

			self.assertTrue(abs(input_np.astype(np.floataa).sum() - input_pt.numpy().astype(np.floataa).sum()) < 1e-2)


		@require_tf
		def 		A__					(				self):
			_UpperCamelCase   :		str			=						self.feature_extraction_class(**self.feat_extract_dict)
			_UpperCamelCase   :		List[Any]			=						self.feat_extract_tester.prepare_inputs_for_common()
			_UpperCamelCase   :		List[Any]			=						feat_extract.model_input_names[0]

			_UpperCamelCase   :		str			=						BatchFeature({input_name: speech_inputs})

			_UpperCamelCase   :		List[str]			=						feat_extract.pad(__snake_case	,       padding='longest'	,       return_tensors='np')[input_name]
			_UpperCamelCase   :		Any			=						feat_extract.pad(__snake_case	,       padding='longest'	,       return_tensors='tf')[input_name]

			self.assertTrue(abs(input_np.astype(np.floataa).sum() - input_tf.numpy().astype(np.floataa).sum()) < 1e-2)


		def 		A__					(				self):
			_UpperCamelCase   :		int			=						self.feat_extract_dict
			_UpperCamelCase   :		str			=						True
			_UpperCamelCase   :		Optional[Any]			=						self.feature_extraction_class(**__snake_case)
			_UpperCamelCase   :		Any			=						self.feat_extract_tester.prepare_inputs_for_common()
			_UpperCamelCase   :		int			=						[len(__snake_case) for x in speech_inputs]
			_UpperCamelCase   :		Optional[int]			=						feat_extract.model_input_names[0]

			_UpperCamelCase   :		Optional[int]			=						BatchFeature({input_name: speech_inputs})

			_UpperCamelCase   :		Any			=						feat_extract.pad(__snake_case	,       padding='longest'	,       return_tensors='np')
			self.assertIn('attention_mask'	,       __snake_case)
			self.assertListEqual(list(processed.attention_mask.shape)	,       list(processed[input_name].shape[:2]))
			self.assertListEqual(processed.attention_mask.sum(-1).tolist()	,       __snake_case)






		def 		A__					(				self):
			_UpperCamelCase   :		List[str]			=						self.feat_extract_dict
			_UpperCamelCase   :		Any			=						True
			_UpperCamelCase   :		Dict			=						self.feature_extraction_class(**__snake_case)
			_UpperCamelCase   :		List[Any]			=						self.feat_extract_tester.prepare_inputs_for_common()
			_UpperCamelCase   :		Union[str, Any]			=						[len(__snake_case) for x in speech_inputs]
			_UpperCamelCase   :		List[Any]			=						feat_extract.model_input_names[0]

			_UpperCamelCase   :		List[Any]			=						BatchFeature({input_name: speech_inputs})
			_UpperCamelCase   :		Tuple			=						min(__snake_case)

			_UpperCamelCase   :		int			=						feat_extract.pad(
			    __snake_case	,       padding='max_length'	,       max_length=__snake_case	,       truncation=__snake_case	,       return_tensors='np')
			self.assertIn('attention_mask'	,       __snake_case)
			self.assertListEqual(
			    list(processed_pad.attention_mask.shape)	,       [processed_pad[input_name].shape[0], max_length])
			self.assertListEqual(
			    processed_pad.attention_mask[:, :max_length].sum(-1).tolist()	,       [max_length for x in speech_inputs])







from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase__								=		logging.get_logger(__name__)

lowerCAmelCase__								=		{
    """RWKV/rwkv-4-169m-pile""": """https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json""",
    """RWKV/rwkv-4-430m-pile""": """https://huggingface.co/RWKV/rwkv-4-430m-pile/resolve/main/config.json""",
    """RWKV/rwkv-4-1b5-pile""": """https://huggingface.co/RWKV/rwkv-4-1b5-pile/resolve/main/config.json""",
    """RWKV/rwkv-4-3b-pile""": """https://huggingface.co/RWKV/rwkv-4-3b-pile/resolve/main/config.json""",
    """RWKV/rwkv-4-7b-pile""": """https://huggingface.co/RWKV/rwkv-4-7b-pile/resolve/main/config.json""",
    """RWKV/rwkv-4-14b-pile""": """https://huggingface.co/RWKV/rwkv-4-14b-pile/resolve/main/config.json""",
    """RWKV/rwkv-raven-1b5""": """https://huggingface.co/RWKV/rwkv-raven-1b5/resolve/main/config.json""",
    """RWKV/rwkv-raven-3b""": """https://huggingface.co/RWKV/rwkv-raven-3b/resolve/main/config.json""",
    """RWKV/rwkv-raven-7b""": """https://huggingface.co/RWKV/rwkv-raven-7b/resolve/main/config.json""",
    """RWKV/rwkv-raven-14b""": """https://huggingface.co/RWKV/rwkv-raven-14b/resolve/main/config.json""",
}







class 	lowercase    (				_lowercase							):


		"""simple docstring"""




		a__					       =  "rwkv"
		a__					       =  {"max_position_embeddings": "context_length"}


		def __init__(				self	,       __snake_case=5_02_77	,       __snake_case=10_24	,       __snake_case=40_96	,       __snake_case=32	,       __snake_case=None	,       __snake_case=None	,       __snake_case=1e-5	,       __snake_case=0	,       __snake_case=0	,       __snake_case=6	,       __snake_case=False	,       __snake_case=True	,       **__snake_case	,       ):
			_UpperCamelCase   :		str			=						vocab_size
			_UpperCamelCase   :		int			=						context_length
			_UpperCamelCase   :		Tuple			=						hidden_size
			_UpperCamelCase   :		Tuple			=						num_hidden_layers
			_UpperCamelCase   :		Dict			=						attention_hidden_size if attention_hidden_size is not None else hidden_size
			_UpperCamelCase   :		Tuple			=						intermediate_size if intermediate_size is not None else 4 * hidden_size
			_UpperCamelCase   :		Union[str, Any]			=						layer_norm_epsilon
			_UpperCamelCase   :		Dict			=						rescale_every
			_UpperCamelCase   :		Optional[Any]			=						use_cache

			_UpperCamelCase   :		str			=						bos_token_id
			_UpperCamelCase   :		Optional[Any]			=						eos_token_id

			super().__init__(
			    tie_word_embeddings=__snake_case	,       bos_token_id=__snake_case	,       eos_token_id=__snake_case	,       **__snake_case)




