import unittest

from parameterized import parameterized

from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaTokenizer


class 	lowerCamelCase_     :






    '''simple docstring'''






    def __init__(	self , __lowercase , __lowercase=13 , __lowercase=7 , __lowercase=True , __lowercase=True , __lowercase=False , __lowercase=True , __lowercase=99 , __lowercase=32 , __lowercase=5 , __lowercase=4 , __lowercase=37 , __lowercase="gelu" , __lowercase=0.1 , __lowercase=0.1 , __lowercase=512 , __lowercase=16 , __lowercase=2 , __lowercase=0.02 , __lowercase=3 , __lowercase=4 , __lowercase=None , )       ->	Dict:
        __UpperCamelCase     :Union[str, Any]			  =		parent
        __UpperCamelCase     :List[str]			  =		batch_size
        __UpperCamelCase     :int			  =		seq_length
        __UpperCamelCase     :str			  =		is_training
        __UpperCamelCase     :Optional[Any]			  =		use_input_mask
        __UpperCamelCase     :List[Any]			  =		use_token_type_ids
        __UpperCamelCase     :Optional[Any]			  =		use_labels
        __UpperCamelCase     :List[Any]			  =		vocab_size
        __UpperCamelCase     :Optional[int]			  =		hidden_size
        __UpperCamelCase     :Tuple			  =		num_hidden_layers
        __UpperCamelCase     :Tuple			  =		num_attention_heads
        __UpperCamelCase     :Optional[int]			  =		intermediate_size
        __UpperCamelCase     :Union[str, Any]			  =		hidden_act
        __UpperCamelCase     :int			  =		hidden_dropout_prob
        __UpperCamelCase     :Optional[int]			  =		attention_probs_dropout_prob
        __UpperCamelCase     :Tuple			  =		max_position_embeddings
        __UpperCamelCase     :str			  =		type_vocab_size
        __UpperCamelCase     :List[Any]			  =		type_sequence_label_size
        __UpperCamelCase     :Optional[int]			  =		initializer_range
        __UpperCamelCase     :str			  =		num_labels
        __UpperCamelCase     :Any			  =		num_choices
        __UpperCamelCase     :Any			  =		scope







    def 			UpperCamelCase__   (	self)       ->	List[Any]:
        __UpperCamelCase     :List[str]			  =		ids_tensor([self.batch_size, self.seq_length] , self.vocab_size)

        __UpperCamelCase     :int			  =		None
        if self.use_input_mask:
            __UpperCamelCase     :Union[str, Any]			  =		random_attention_mask([self.batch_size, self.seq_length])

        __UpperCamelCase     :str			  =		None
        if self.use_token_type_ids:
            __UpperCamelCase     :Dict			  =		ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size)

        __UpperCamelCase     :Optional[Any]			  =		None
        __UpperCamelCase     :Dict			  =		None
        __UpperCamelCase     :Any			  =		None
        if self.use_labels:
            __UpperCamelCase     :List[Any]			  =		ids_tensor([self.batch_size] , self.type_sequence_label_size)
            __UpperCamelCase     :Union[str, Any]			  =		ids_tensor([self.batch_size, self.seq_length] , self.num_labels)
            __UpperCamelCase     :Optional[int]			  =		ids_tensor([self.batch_size] , self.num_choices)

        __UpperCamelCase     :int			  =		self.get_config()

        return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels







    def 			UpperCamelCase__   (	self)       ->	str:
        return LlamaConfig(
            vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=__lowercase , initializer_range=self.initializer_range , )







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase)       ->	Tuple:
        __UpperCamelCase     :str			  =		LlamaModel(config=__lowercase)
        model.to(__lowercase)
        model.eval()
        __UpperCamelCase     :str			  =		model(__lowercase , attention_mask=__lowercase)
        __UpperCamelCase     :Any			  =		model(__lowercase)
        self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size))







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , )       ->	Tuple:
        __UpperCamelCase     :Optional[Any]			  =		True
        __UpperCamelCase     :Dict			  =		LlamaModel(__lowercase)
        model.to(__lowercase)
        model.eval()
        __UpperCamelCase     :int			  =		model(
            __lowercase , attention_mask=__lowercase , encoder_hidden_states=__lowercase , encoder_attention_mask=__lowercase , )
        __UpperCamelCase     :Any			  =		model(
            __lowercase , attention_mask=__lowercase , encoder_hidden_states=__lowercase , )
        __UpperCamelCase     :Tuple			  =		model(__lowercase , attention_mask=__lowercase)
        self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size))







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , )       ->	Optional[Any]:
        __UpperCamelCase     :Optional[int]			  =		LlamaForCausalLM(config=__lowercase)
        model.to(__lowercase)
        model.eval()
        __UpperCamelCase     :Any			  =		model(__lowercase , attention_mask=__lowercase , labels=__lowercase)
        self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size))







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , )       ->	List[Any]:
        __UpperCamelCase     :str			  =		True
        __UpperCamelCase     :Tuple			  =		True
        __UpperCamelCase     :List[Any]			  =		LlamaForCausalLM(config=__lowercase)
        model.to(__lowercase)
        model.eval()

        # first forward pass
        __UpperCamelCase     :Union[str, Any]			  =		model(
            __lowercase , attention_mask=__lowercase , encoder_hidden_states=__lowercase , encoder_attention_mask=__lowercase , use_cache=__lowercase , )
        __UpperCamelCase     :Optional[Any]			  =		outputs.past_key_values

        # create hypothetical multiple next token and extent to next_input_ids
        __UpperCamelCase     :Optional[int]			  =		ids_tensor((self.batch_size, 3) , config.vocab_size)
        __UpperCamelCase     :int			  =		ids_tensor((self.batch_size, 3) , vocab_size=2)

        # append to next input_ids and
        __UpperCamelCase     :Any			  =		torch.cat([input_ids, next_tokens] , dim=-1)
        __UpperCamelCase     :Optional[int]			  =		torch.cat([input_mask, next_mask] , dim=-1)

        __UpperCamelCase     :Union[str, Any]			  =		model(
            __lowercase , attention_mask=__lowercase , encoder_hidden_states=__lowercase , encoder_attention_mask=__lowercase , output_hidden_states=__lowercase , )['''hidden_states'''][0]
        __UpperCamelCase     :Tuple			  =		model(
            __lowercase , attention_mask=__lowercase , encoder_hidden_states=__lowercase , encoder_attention_mask=__lowercase , past_key_values=__lowercase , output_hidden_states=__lowercase , )['''hidden_states'''][0]

        # select random slice
        __UpperCamelCase     :Any			  =		ids_tensor((1,) , output_from_past.shape[-1]).item()
        __UpperCamelCase     :Optional[Any]			  =		output_from_no_past[:, -3:, random_slice_idx].detach()
        __UpperCamelCase     :Union[str, Any]			  =		output_from_past[:, :, random_slice_idx].detach()

        self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])

        # test that outputs are equal for slice
        self.parent.assertTrue(torch.allclose(__lowercase , __lowercase , atol=1E-3))







    def 			UpperCamelCase__   (	self)       ->	str:
        __UpperCamelCase     :Union[str, Any]			  =		self.prepare_config_and_inputs()
        (
            (
            __UpperCamelCase
        )  ,       (
            __UpperCamelCase
        )  ,       (
            __UpperCamelCase
        )  ,       (
            __UpperCamelCase
        )  ,       (
            __UpperCamelCase
        )  ,       (
            __UpperCamelCase
        )  ,       (
            __UpperCamelCase
        )  ,       
        )     :List[Any]			  =		config_and_inputs
        __UpperCamelCase     :Any			  =		{'''input_ids''': input_ids, '''attention_mask''': input_mask}
        return config, inputs_dict


@require_torch
class 	lowerCamelCase_     (   UpperCAmelCase_						,	UpperCAmelCase_						,	UpperCAmelCase_						,	unittest.TestCase ):






    '''simple docstring'''






    a__	:						Union[str, Any]						=      (LlamaModel, LlamaForCausalLM, LlamaForSequenceClassification) if is_torch_available() else ()
    a__	:						Union[str, Any]						=      (LlamaForCausalLM,) if is_torch_available() else ()
    a__	:						List[Any]						=      (
        {
            """feature-extraction""": LlamaModel,
            """text-classification""": LlamaForSequenceClassification,
            """text-generation""": LlamaForCausalLM,
            """zero-shot""": LlamaForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )
    a__	:						Tuple						=      False
    a__	:						Any						=      False







    def 			UpperCamelCase__   (	self)       ->	str:
        __UpperCamelCase     :Dict			  =		LlamaModelTester(self)
        __UpperCamelCase     :Union[str, Any]			  =		ConfigTester(self , config_class=__lowercase , hidden_size=37)







    def 			UpperCamelCase__   (	self)       ->	Dict:
        self.config_tester.run_common_tests()







    def 			UpperCamelCase__   (	self)       ->	Tuple:
        __UpperCamelCase     :int			  =		self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__lowercase)







    def 			UpperCamelCase__   (	self)       ->	Union[str, Any]:
        __UpperCamelCase     :List[str]			  =		self.model_tester.prepare_config_and_inputs()
        for type in ["absolute", "relative_key", "relative_key_query"]:
            __UpperCamelCase     :Any			  =		type
            self.model_tester.create_and_check_model(*__lowercase)







    def 			UpperCamelCase__   (	self)       ->	List[str]:
        __UpperCamelCase  ,       __UpperCamelCase     :Union[str, Any]			  =		self.model_tester.prepare_config_and_inputs_for_common()
        __UpperCamelCase     :Dict			  =		3
        __UpperCamelCase     :int			  =		input_dict['''input_ids''']
        __UpperCamelCase     :Tuple			  =		input_ids.ne(1).to(__lowercase)
        __UpperCamelCase     :Any			  =		ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size)
        __UpperCamelCase     :str			  =		LlamaForSequenceClassification(__lowercase)
        model.to(__lowercase)
        model.eval()
        __UpperCamelCase     :str			  =		model(__lowercase , attention_mask=__lowercase , labels=__lowercase)
        self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels))







    def 			UpperCamelCase__   (	self)       ->	int:
        __UpperCamelCase  ,       __UpperCamelCase     :str			  =		self.model_tester.prepare_config_and_inputs_for_common()
        __UpperCamelCase     :Optional[int]			  =		3
        __UpperCamelCase     :Optional[int]			  =		'''single_label_classification'''
        __UpperCamelCase     :List[str]			  =		input_dict['''input_ids''']
        __UpperCamelCase     :str			  =		input_ids.ne(1).to(__lowercase)
        __UpperCamelCase     :Optional[int]			  =		ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size)
        __UpperCamelCase     :Tuple			  =		LlamaForSequenceClassification(__lowercase)
        model.to(__lowercase)
        model.eval()
        __UpperCamelCase     :List[Any]			  =		model(__lowercase , attention_mask=__lowercase , labels=__lowercase)
        self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels))







    def 			UpperCamelCase__   (	self)       ->	Dict:
        __UpperCamelCase  ,       __UpperCamelCase     :Dict			  =		self.model_tester.prepare_config_and_inputs_for_common()
        __UpperCamelCase     :int			  =		3
        __UpperCamelCase     :Dict			  =		'''multi_label_classification'''
        __UpperCamelCase     :Optional[int]			  =		input_dict['''input_ids''']
        __UpperCamelCase     :int			  =		input_ids.ne(1).to(__lowercase)
        __UpperCamelCase     :Optional[int]			  =		ids_tensor(
            [self.model_tester.batch_size, config.num_labels] , self.model_tester.type_sequence_label_size).to(torch.float)
        __UpperCamelCase     :str			  =		LlamaForSequenceClassification(__lowercase)
        model.to(__lowercase)
        model.eval()
        __UpperCamelCase     :Any			  =		model(__lowercase , attention_mask=__lowercase , labels=__lowercase)
        self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels))







    @unittest.skip('''LLaMA buffers include complex numbers, which breaks this test''')
    def 			UpperCamelCase__   (	self)       ->	Optional[Any]:
        pass







    @parameterized.expand([('''linear''',), ('''dynamic''',)])
    def 			UpperCamelCase__   (	self , __lowercase)       ->	Optional[int]:
        __UpperCamelCase  ,       __UpperCamelCase     :List[str]			  =		self.model_tester.prepare_config_and_inputs_for_common()
        __UpperCamelCase     :Union[str, Any]			  =		ids_tensor([1, 10] , config.vocab_size)
        __UpperCamelCase     :List[Any]			  =		ids_tensor([1, int(config.max_position_embeddings * 1.5)] , config.vocab_size)

        set_seed(42)  # Fixed seed at init time so the two models get the same random weights
        __UpperCamelCase     :str			  =		LlamaModel(__lowercase)
        original_model.to(__lowercase)
        original_model.eval()
        __UpperCamelCase     :Optional[Any]			  =		original_model(__lowercase).last_hidden_state
        __UpperCamelCase     :Optional[int]			  =		original_model(__lowercase).last_hidden_state

        set_seed(42)  # Fixed seed at init time so the two models get the same random weights
        __UpperCamelCase     :List[str]			  =		{'''type''': scaling_type, '''factor''': 10.0}
        __UpperCamelCase     :int			  =		LlamaModel(__lowercase)
        scaled_model.to(__lowercase)
        scaled_model.eval()
        __UpperCamelCase     :List[str]			  =		scaled_model(__lowercase).last_hidden_state
        __UpperCamelCase     :List[Any]			  =		scaled_model(__lowercase).last_hidden_state

        # Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
        # maximum sequence length, so the outputs for the short input should match.
        if scaling_type == "dynamic":
            self.assertTrue(torch.allclose(__lowercase , __lowercase , atol=1E-5))
        else:
            self.assertFalse(torch.allclose(__lowercase , __lowercase , atol=1E-5))

        # The output should be different for long inputs
        self.assertFalse(torch.allclose(__lowercase , __lowercase , atol=1E-5))


@require_torch
class 	lowerCamelCase_     (   unittest.TestCase ):






    '''simple docstring'''






    @unittest.skip('''Logits are not exactly the same, once we fix the instabalities somehow, will update!''')
    @slow
    def 			UpperCamelCase__   (	self)       ->	List[str]:
        __UpperCamelCase     :Dict			  =		[1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
        __UpperCamelCase     :Optional[Any]			  =		LlamaForCausalLM.from_pretrained('''meta-llama/Llama-2-7b-hf''' , device_map='''auto''')
        __UpperCamelCase     :Tuple			  =		model(torch.tensor([input_ids]))
        # Expected mean on dim = -1
        __UpperCamelCase     :Any			  =		torch.tensor([[-6.65_50, -4.12_27, -4.98_59, -3.24_06, 0.82_62, -3.00_33, 1.29_64, -3.36_99]])
        torch.testing.assert_close(out.mean(-1) , __lowercase , atol=1E-2 , rtol=1E-2)
        # slicing logits[0, 0, 0:30]
        # fmt: off
        __UpperCamelCase     :Any			  =		torch.tensor([-12.82_81, -7.44_53, -0.46_39, -8.06_25, -7.25_00, -8.00_00, -6.48_83, -7.76_95, -7.84_38, -7.03_12, -6.21_88, -7.13_28, -1.84_96, 1.99_61, -8.62_50, -6.72_27, -12.82_81, -6.94_92, -7.07_42, -7.78_52, -7.58_20, -7.90_62, -6.93_75, -7.98_05, -8.34_38, -8.15_62, -8.04_69, -7.62_50, -7.74_22, -7.33_98,])
        # fmt: on
        torch.testing.assert_close(out[0, 0, :30] , __lowercase , atol=1E-5 , rtol=1E-5)







    @unittest.skip('''Logits are not exactly the same, once we fix the instabalities somehow, will update!''')
    @slow
    def 			UpperCamelCase__   (	self)       ->	List[Any]:
        __UpperCamelCase     :str			  =		[1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
        __UpperCamelCase     :Any			  =		LlamaForCausalLM.from_pretrained('''meta-llama/Llama-2-13b-hf''' , device_map='''auto''')
        __UpperCamelCase     :Optional[int]			  =		model(torch.tensor(__lowercase))
        # Expected mean on dim = -1
        __UpperCamelCase     :Any			  =		torch.tensor([[-2.06_22, -1.27_94, -1.16_38, -0.97_88, -1.46_03, -1.02_38, -1.78_93, -1.44_11]])
        torch.testing.assert_close(out.mean(-1) , __lowercase , atol=1E-2 , rtol=1E-2)
        # slicing logits[0, 0, 0:30]
        # fmt: off
        __UpperCamelCase     :Union[str, Any]			  =		torch.tensor([-8.14_06, -8.05_47, 2.74_61, -1.23_44, -0.14_48, -1.82_62, -1.00_20, -1.81_54, -1.68_95, -1.85_16, -2.35_74, -0.92_77, 3.75_98, 6.57_42, -1.29_98, -0.11_77, -8.14_06, -2.96_88, -2.91_99, -3.16_99, -3.52_54, -2.35_55, -2.79_88, -3.41_41, -2.82_62, -4.51_95, -3.33_79, -3.31_64, -2.78_32, -3.02_73])
        # fmt: on
        torch.testing.assert_close(out[0, 0, :30] , __lowercase , atol=1E-5 , rtol=1E-5)







    @unittest.skip('''Logits are not exactly the same, once we fix the instabalities somehow, will update!''')
    @slow
    def 			UpperCamelCase__   (	self)       ->	int:
        __UpperCamelCase     :Any			  =		[1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
        __UpperCamelCase     :Optional[int]			  =		LlamaForCausalLM.from_pretrained('''meta-llama/Llama-2-13b-chat-hf''' , device_map='''auto''')
        __UpperCamelCase     :str			  =		model(torch.tensor(__lowercase))
        # Expected mean on dim = -1
        __UpperCamelCase     :List[Any]			  =		torch.tensor([[-0.85_62, -1.85_20, -0.75_51, -0.41_62, -1.51_61, -1.20_38, -2.48_23, -2.32_54]])
        torch.testing.assert_close(out.mean(-1) , __lowercase , atol=1E-2 , rtol=1E-2)
        # slicing logits[0, 0, 0:30]
        # fmt: off
        __UpperCamelCase     :Union[str, Any]			  =		torch.tensor([-2.22_27, 4.88_28, 0.90_23, -0.45_78, -0.78_71, -0.10_33, -0.62_21, -0.57_86, -0.78_03, -1.06_74, -1.29_20, -0.15_70, 0.80_08, 2.07_23, -0.94_97, 0.27_71, -2.22_27, -0.76_12, -1.43_46, -1.20_61, -1.64_26, -0.30_00, -0.71_39, -1.19_34, -1.86_91, -1.69_73, -1.59_47, -1.27_05, -0.35_23, -0.55_13])
        # fmt: on
        torch.testing.assert_close(out.mean(-1) , __lowercase , atol=1E-2 , rtol=1E-2)







    @unittest.skip(
        '''Logits are not exactly the same, once we fix the instabalities somehow, will update! Also it is gonna be a `too_slow` test''')
    @slow
    def 			UpperCamelCase__   (	self)       ->	Tuple:
        __UpperCamelCase     :Any			  =		[1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
        __UpperCamelCase     :List[Any]			  =		LlamaForCausalLM.from_pretrained('''meta-llama/Llama-2-70b-hf''' , device_map='''auto''')
        __UpperCamelCase     :List[str]			  =		model(torch.tensor(__lowercase))

        __UpperCamelCase     :Dict			  =		torch.tensor(
            [[-4.23_27, -3.33_60, -4.66_65, -4.76_31, -1.81_80, -3.41_70, -1.42_11, -3.18_10]] , dtype=torch.floataa)
        torch.testing.assert_close(out.mean(-1) , __lowercase , atol=1E-2 , rtol=1E-2)
        # fmt: off
        __UpperCamelCase     :str			  =		torch.tensor([-9.49_22, -3.95_51, 1.79_98, -5.67_58, -5.10_55, -5.89_84, -4.83_20, -6.80_86, -6.53_91, -5.61_72, -5.58_20, -5.53_52, 1.78_81, 3.62_89, -6.51_17, -3.47_85, -9.50_00, -6.03_52, -6.81_25, -6.01_95, -6.68_36, -5.47_27, -6.28_12, -6.03_91, -7.33_98, -7.42_97, -7.48_44, -6.58_20, -5.87_89, -5.53_12])
        # fmt: on
        torch.testing.assert_close(out[0, 0, :30] , __lowercase , atol=1E-5 , rtol=1E-5)







    @unittest.skip('''Model is curently gated''')
    @slow
    def 			UpperCamelCase__   (	self)       ->	str:
        __UpperCamelCase     :List[Any]			  =		'''Simply put, the theory of relativity states that 1) the laws of physics are the same everywhere in the universe and 2) the passage of time and the length of objects can vary depending on the observer\'s frame of reference.\n\nThe first part of the theory, that the laws of physics are the same everywhere, is known as the "princi'''
        __UpperCamelCase     :Optional[int]			  =		'''Simply put, the theory of relativity states that '''
        __UpperCamelCase     :Tuple			  =		LlamaTokenizer.from_pretrained('''meta-llama/Llama-2-13b-chat-hf''')
        __UpperCamelCase     :Dict			  =		tokenizer.encode(__lowercase , return_tensors='''pt''')
        __UpperCamelCase     :Tuple			  =		LlamaForCausalLM.from_pretrained(
            '''meta-llama/Llama-2-13b-chat-hf''' , device_map='''sequential''' , use_safetensors=__lowercase)

        # greedy generation outputs
        __UpperCamelCase     :List[Any]			  =		model.generate(__lowercase , max_new_tokens=64 , top_p=__lowercase , temperature=1 , do_sample=__lowercase)
        __UpperCamelCase     :str			  =		tokenizer.decode(generated_ids[0] , skip_special_tokens=__lowercase)
        self.assertEqual(__lowercase , __lowercase)











from typing import List, Optional, TypeVar

from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interleave_iterable_datasets
from .splits import NamedSplit
from .utils import logging
from .utils.py_utils import Literal


snake_case_ : Any    		=       logging.get_logger(__name__)


snake_case_ : Optional[int]    		=       TypeVar("DatasetType", Dataset, IterableDataset)


def    A						(__A   :    List[DatasetType]   ,				__A   :    Optional[List[float]] = None   ,				__A   :    Optional[int] = None   ,				__A   :    Optional[DatasetInfo] = None   ,				__A   :    Optional[NamedSplit] = None   ,				__A   :    Literal["first_exhausted", "all_exhausted"] = "first_exhausted"   ,				)   ->      DatasetType:







     """simple docstring"""


     from .arrow_dataset import Dataset
     from .iterable_dataset import IterableDataset

     if not datasets:
          raise ValueError('''Unable to interleave an empty list of datasets.'''       )
     for i, dataset in enumerate(__A       ):
          if not isinstance(__A   ,				(Dataset, IterableDataset)       ):
               if isinstance(__A   ,				(DatasetDict, IterableDatasetDict)       ):
                    if not dataset:
                         raise ValueError(
                             F"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} """
                             '''is an empty dataset dictionary.'''       )
                    raise ValueError(
                        F"""Dataset at position {i} has at least one split: {list(__A       )}\n"""
                        F"""Please pick one to interleave with the other datasets, for example: dataset['{next(iter(__A       )       )}']"""       )
               raise ValueError(
                   F"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(__A       ).__name__}."""       )
          if i == 0:
               UpperCAmelCase_     ,       UpperCAmelCase_          =							(
                   (Dataset, IterableDataset) if isinstance(__A   ,				__A       ) else (IterableDataset, Dataset)
               )
          elif not isinstance(__A   ,				__A       ):
               raise ValueError(
                   F"""Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects."""       )
     if stopping_strategy not in ["first_exhausted", "all_exhausted"]:
          raise ValueError(F"""{stopping_strategy} is not supported. Please enter a valid stopping_strategy."""       )
     if dataset_type is Dataset:
          return _interleave_map_style_datasets(
              __A   ,				__A   ,				__A   ,				info=__A   ,				split=__A   ,				stopping_strategy=__A       )
     else:
          return _interleave_iterable_datasets(
              __A   ,				__A   ,				__A   ,				info=__A   ,				split=__A   ,				stopping_strategy=__A       )


def    A						(__A   :    List[DatasetType]   ,				__A   :    Optional[DatasetInfo] = None   ,				__A   :    Optional[NamedSplit] = None   ,				__A   :    int = 0   ,				)   ->      DatasetType:







     """simple docstring"""


     if not dsets:
          raise ValueError('''Unable to concatenate an empty list of datasets.'''       )
     for i, dataset in enumerate(__A       ):
          if not isinstance(__A   ,				(Dataset, IterableDataset)       ):
               if isinstance(__A   ,				(DatasetDict, IterableDatasetDict)       ):
                    if not dataset:
                         raise ValueError(
                             F"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} """
                             '''is an empty dataset dictionary.'''       )
                    raise ValueError(
                        F"""Dataset at position {i} has at least one split: {list(__A       )}\n"""
                        F"""Please pick one to interleave with the other datasets, for example: dataset['{next(iter(__A       )       )}']"""       )
               raise ValueError(
                   F"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(__A       ).__name__}."""       )
          if i == 0:
               UpperCAmelCase_     ,       UpperCAmelCase_          =							(
                   (Dataset, IterableDataset) if isinstance(__A   ,				__A       ) else (IterableDataset, Dataset)
               )
          elif not isinstance(__A   ,				__A       ):
               raise ValueError(
                   F"""Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects."""       )
     if dataset_type is Dataset:
          return _concatenate_map_style_datasets(__A   ,				info=__A   ,				split=__A   ,				axis=__A       )
     else:
          return _concatenate_iterable_datasets(__A   ,				info=__A   ,				split=__A   ,				axis=__A       )






