import unittest

from parameterized import parameterized

from transformers import AutoTokenizer, GPTNeoXConfig, 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 (
        GPTNeoXForCausalLM,
        GPTNeoXForQuestionAnswering,
        GPTNeoXForSequenceClassification,
        GPTNeoXForTokenClassification,
        GPTNeoXModel,
    )


class      lowerCamelCase_					:






    '''simple docstring'''






    def __init__(     self			,			__lowercase			,			__lowercase=13			,			__lowercase=7			,			__lowercase=True			,			__lowercase=True			,			__lowercase=True			,			__lowercase=True			,			__lowercase=99			,			__lowercase=64			,			__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			,			)	->     Union[str, Any]:
        __UpperCamelCase					:Optional[int]        =		parent
        __UpperCamelCase					:Dict        =		batch_size
        __UpperCamelCase					:Optional[int]        =		seq_length
        __UpperCamelCase					:List[str]        =		is_training
        __UpperCamelCase					:Tuple        =		use_input_mask
        __UpperCamelCase					:str        =		use_token_type_ids
        __UpperCamelCase					:Optional[int]        =		use_labels
        __UpperCamelCase					:Optional[int]        =		vocab_size
        __UpperCamelCase					:str        =		hidden_size
        __UpperCamelCase					:Union[str, Any]        =		num_hidden_layers
        __UpperCamelCase					:Optional[int]        =		num_attention_heads
        __UpperCamelCase					:str        =		intermediate_size
        __UpperCamelCase					:List[str]        =		hidden_act
        __UpperCamelCase					:Optional[int]        =		hidden_dropout_prob
        __UpperCamelCase					:Dict        =		attention_probs_dropout_prob
        __UpperCamelCase					:List[str]        =		max_position_embeddings
        __UpperCamelCase					:Optional[int]        =		type_vocab_size
        __UpperCamelCase					:Dict        =		type_sequence_label_size
        __UpperCamelCase					:int        =		initializer_range
        __UpperCamelCase					:List[str]        =		num_labels
        __UpperCamelCase					:Optional[int]        =		num_choices
        __UpperCamelCase					:List[Any]        =		scope
        __UpperCamelCase					:Union[str, Any]        =		vocab_size - 1







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

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

        __UpperCamelCase					:Optional[int]        =		None
        if self.use_labels:
            __UpperCamelCase					:Any        =		ids_tensor([self.batch_size, self.seq_length]			,			self.num_labels)

        __UpperCamelCase					:str        =		self.get_config()

        return config, input_ids, input_mask, token_labels







    def       UpperCamelCase__       (     self)	->     List[Any]:
        return GPTNeoXConfig(
            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			,			pad_token_id=self.pad_token_id			,			)







    def       UpperCamelCase__       (     self)	->     Optional[int]:
        __UpperCamelCase					:str        =		self.prepare_config_and_inputs()

        __UpperCamelCase					:int        =		True

        return config, input_ids, input_mask, token_labels







    def       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase			,			__lowercase)	->     List[str]:
        __UpperCamelCase					:Any        =		GPTNeoXModel(config=__lowercase)
        model.to(__lowercase)
        model.eval()
        __UpperCamelCase					:int        =		model(__lowercase			,			attention_mask=__lowercase)
        __UpperCamelCase					:Tuple        =		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)	->     str:
        __UpperCamelCase					:str        =		True
        __UpperCamelCase					:Union[str, Any]        =		GPTNeoXModel(__lowercase)
        model.to(__lowercase)
        model.eval()
        __UpperCamelCase					:Optional[int]        =		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)	->     List[Any]:
        __UpperCamelCase					:int        =		GPTNeoXForCausalLM(config=__lowercase)
        model.to(__lowercase)
        model.eval()
        __UpperCamelCase					:Dict        =		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)	->     List[Any]:
        __UpperCamelCase					:Any        =		self.num_labels
        __UpperCamelCase					:List[Any]        =		GPTNeoXForQuestionAnswering(__lowercase)
        model.to(__lowercase)
        model.eval()
        __UpperCamelCase					:str        =		model(__lowercase			,			attention_mask=__lowercase)
        self.parent.assertEqual(result.start_logits.shape			,			(self.batch_size, self.seq_length))
        self.parent.assertEqual(result.end_logits.shape			,			(self.batch_size, self.seq_length))







    def       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase			,			__lowercase			,			__lowercase)	->     List[str]:
        __UpperCamelCase					:Optional[Any]        =		self.num_labels
        __UpperCamelCase					:Any        =		GPTNeoXForSequenceClassification(__lowercase)
        model.to(__lowercase)
        model.eval()
        __UpperCamelCase					:Optional[int]        =		ids_tensor([self.batch_size]			,			self.type_sequence_label_size)
        __UpperCamelCase					:Optional[Any]        =		model(__lowercase			,			attention_mask=__lowercase			,			labels=__lowercase)
        self.parent.assertEqual(result.logits.shape			,			(self.batch_size, self.num_labels))







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







    def       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase			,			__lowercase)	->     Dict:
        __UpperCamelCase					:Optional[int]        =		True
        __UpperCamelCase					:List[str]        =		GPTNeoXForCausalLM(config=__lowercase)
        model.to(__lowercase)
        model.eval()

        # first forward pass
        __UpperCamelCase					:Tuple        =		model(__lowercase			,			attention_mask=__lowercase			,			use_cache=__lowercase)
        __UpperCamelCase					:str        =		outputs.past_key_values

        # create hypothetical multiple next token and extent to next_input_ids
        __UpperCamelCase					:int        =		ids_tensor((self.batch_size, 3)			,			config.vocab_size)
        __UpperCamelCase					:Dict        =		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					:Optional[int]        =		model(__lowercase			,			attention_mask=__lowercase			,			output_hidden_states=__lowercase)
        __UpperCamelCase					:List[str]        =		output_from_no_past['''hidden_states'''][0]
        __UpperCamelCase					:Dict        =		model(
            __lowercase			,			attention_mask=__lowercase			,			past_key_values=__lowercase			,			output_hidden_states=__lowercase			,			)['''hidden_states'''][0]

        # select random slice
        __UpperCamelCase					:List[str]        =		ids_tensor((1,)			,			output_from_past.shape[-1]).item()
        __UpperCamelCase					:Optional[int]        =		output_from_no_past[:, -3:, random_slice_idx].detach()
        __UpperCamelCase					:Dict        =		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)	->     List[str]:
        __UpperCamelCase					:Optional[Any]        =		self.prepare_config_and_inputs()
        __UpperCamelCase					:Optional[int]        =		config_and_inputs
        __UpperCamelCase					:List[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__     :						str      =				(
        (
            GPTNeoXModel,
            GPTNeoXForCausalLM,
            GPTNeoXForQuestionAnswering,
            GPTNeoXForSequenceClassification,
            GPTNeoXForTokenClassification,
        )
        if is_torch_available()
        else ()
    )
    a__     :						Union[str, Any]      =				(GPTNeoXForCausalLM,) if is_torch_available() else ()
    a__     :						Tuple      =				(
        {
            """feature-extraction""": GPTNeoXModel,
            """question-answering""": GPTNeoXForQuestionAnswering,
            """text-classification""": GPTNeoXForSequenceClassification,
            """text-generation""": GPTNeoXForCausalLM,
            """token-classification""": GPTNeoXForTokenClassification,
            """zero-shot""": GPTNeoXForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )
    a__     :						Optional[int]      =				False
    a__     :						Union[str, Any]      =				False
    a__     :						Any      =				False
    a__     :						str      =				False







    def       UpperCamelCase__       (     self)	->     List[str]:
        __UpperCamelCase					:str        =		GPTNeoXModelTester(self)
        __UpperCamelCase					:Union[str, Any]        =		ConfigTester(self			,			config_class=__lowercase			,			hidden_size=64			,			num_attention_heads=8)







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







    def       UpperCamelCase__       (     self)	->     Optional[Any]:
        __UpperCamelCase					:Optional[Any]        =		self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(__lowercase			,			__lowercase			,			__lowercase)







    def       UpperCamelCase__       (     self)	->     Optional[Any]:
        __UpperCamelCase					:Optional[int]        =		self.model_tester.prepare_config_and_inputs_for_decoder()
        self.model_tester.create_and_check_model_as_decoder(__lowercase			,			__lowercase			,			__lowercase)







    def       UpperCamelCase__       (     self)	->     str:
        # This regression test was failing with PyTorch < 1.3
        __UpperCamelCase					:str        =		self.model_tester.prepare_config_and_inputs_for_decoder()

        __UpperCamelCase					:int        =		None

        self.model_tester.create_and_check_model_as_decoder(__lowercase			,			__lowercase			,			__lowercase)







    def       UpperCamelCase__       (     self)	->     Union[str, Any]:
        __UpperCamelCase					:Optional[Any]        =		self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_decoder_model_past_large_inputs(__lowercase			,			__lowercase			,			__lowercase)







    def       UpperCamelCase__       (     self)	->     Optional[int]:
        __UpperCamelCase					:Union[str, Any]        =		self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_causal_lm(*__lowercase)







    def       UpperCamelCase__       (     self)	->     Optional[Any]:
        __UpperCamelCase					:Any        =		self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_question_answering(*__lowercase)







    def       UpperCamelCase__       (     self)	->     Union[str, Any]:
        __UpperCamelCase					:str        =		self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_sequence_classification(*__lowercase)







    def       UpperCamelCase__       (     self)	->     Optional[Any]:
        __UpperCamelCase					:List[Any]        =		self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_token_classification(*__lowercase)







    @unittest.skip(reason='''Feed forward chunking is not implemented''')
    def       UpperCamelCase__       (     self)	->     Optional[int]:
        pass







    @parameterized.expand([('''linear''',), ('''dynamic''',)])
    def       UpperCamelCase__       (     self			,			__lowercase)	->     List[str]:
        __UpperCamelCase					:Union[str, Any]        =		self.model_tester.prepare_config_and_inputs_for_common()
        __UpperCamelCase					:Tuple        =		ids_tensor([1, 10]			,			config.vocab_size)
        __UpperCamelCase					:List[str]        =		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					:Tuple        =		GPTNeoXModel(__lowercase)
        original_model.to(__lowercase)
        original_model.eval()
        __UpperCamelCase					:Dict        =		original_model(__lowercase).last_hidden_state
        __UpperCamelCase					:Tuple        =		original_model(__lowercase).last_hidden_state

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






    @slow
    def       UpperCamelCase__       (     self)	->     List[str]:
        __UpperCamelCase					:Union[str, Any]        =		AutoTokenizer.from_pretrained('''EleutherAI/pythia-410m-deduped''')
        for checkpointing in [True, False]:
            __UpperCamelCase					:Optional[int]        =		GPTNeoXForCausalLM.from_pretrained('''EleutherAI/pythia-410m-deduped''')

            if checkpointing:
                model.gradient_checkpointing_enable()
            else:
                model.gradient_checkpointing_disable()
            model.to(__lowercase)

            __UpperCamelCase					:List[Any]        =		tokenizer('''My favorite food is'''			,			return_tensors='''pt''').to(__lowercase)
            # The hub repo. is updated on 2023-04-04, resulting in poor outputs.
            # See: https://github.com/huggingface/transformers/pull/24193
            __UpperCamelCase					:Dict        =		'''My favorite food is a good old-fashioned, old-fashioned, old-fashioned.\n\nI\'m not sure'''

            __UpperCamelCase					:List[str]        =		model.generate(**__lowercase			,			do_sample=__lowercase			,			max_new_tokens=20)
            __UpperCamelCase					:Union[str, Any]        =		tokenizer.batch_decode(__lowercase)[0]

            self.assertEqual(__lowercase			,			__lowercase)






from __future__ import annotations



def lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    print(f"""Vertex\tShortest Distance from vertex {src}"""     )
    for i, d in enumerate(SCREAMING_SNAKE_CASE     ):
        print(f"""{i}\t\t{d}"""     )



def lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    for j in range(SCREAMING_SNAKE_CASE     ):
        __UpperCamelCase  ,	__UpperCamelCase  ,	__UpperCamelCase					:int        =		(graph[j][k] for k in ['''src''', '''dst''', '''weight'''])
        if distance[u] != float('''inf'''     ) and distance[u] + w < distance[v]:
            return True
    return False



def lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    __UpperCamelCase					:List[Any]        =		[float('''inf'''     )] * vertex_count
    __UpperCamelCase					:str        =		0.0

    for _ in range(vertex_count - 1     ):
        for j in range(SCREAMING_SNAKE_CASE     ):
            __UpperCamelCase  ,	__UpperCamelCase  ,	__UpperCamelCase					:Dict        =		(graph[j][k] for k in ['''src''', '''dst''', '''weight'''])

            if distance[u] != float('''inf'''     ) and distance[u] + w < distance[v]:
                __UpperCamelCase					:Any        =		distance[u] + w

    __UpperCamelCase					:Tuple        =		check_negative_cycle(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     )
    if negative_cycle_exists:
        raise Exception('''Negative cycle found'''     )

    return distance


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    __lowercase										= int(input('''Enter number of vertices: ''').strip())
    __lowercase										= int(input('''Enter number of edges: ''').strip())

    __lowercase										= [{} for _ in range(E)]

    for i in range(E):
        print('''Edge ''', i + 1)
        __lowercase			,	__lowercase			,	__lowercase										= (
            int(x)
            for x in input('''Enter source, destination, weight: ''').strip().split(''' ''')
        )
        __lowercase										= {'''src''': src, '''dst''': dest, '''weight''': weight}

    __lowercase										= int(input('''\nEnter shortest path source:''').strip())
    __lowercase										= bellman_ford(graph, V, E, source)
    print_distance(shortest_distance, 0)





