





from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


SCREAMING_SNAKE_CASE_   :      Optional[int]			=   logging.get_logger(__name__)


SCREAMING_SNAKE_CASE_   :      Any			=   {
    '''Salesforce/codegen-350M-nl''': '''https://huggingface.co/Salesforce/codegen-350M-nl/resolve/main/config.json''',
    '''Salesforce/codegen-350M-multi''': '''https://huggingface.co/Salesforce/codegen-350M-multi/resolve/main/config.json''',
    '''Salesforce/codegen-350M-mono''': '''https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/config.json''',
    '''Salesforce/codegen-2B-nl''': '''https://huggingface.co/Salesforce/codegen-2B-nl/resolve/main/config.json''',
    '''Salesforce/codegen-2B-multi''': '''https://huggingface.co/Salesforce/codegen-2B-multi/resolve/main/config.json''',
    '''Salesforce/codegen-2B-mono''': '''https://huggingface.co/Salesforce/codegen-2B-mono/resolve/main/config.json''',
    '''Salesforce/codegen-6B-nl''': '''https://huggingface.co/Salesforce/codegen-6B-nl/resolve/main/config.json''',
    '''Salesforce/codegen-6B-multi''': '''https://huggingface.co/Salesforce/codegen-6B-multi/resolve/main/config.json''',
    '''Salesforce/codegen-6B-mono''': '''https://huggingface.co/Salesforce/codegen-6B-mono/resolve/main/config.json''',
    '''Salesforce/codegen-16B-nl''': '''https://huggingface.co/Salesforce/codegen-16B-nl/resolve/main/config.json''',
    '''Salesforce/codegen-16B-multi''': '''https://huggingface.co/Salesforce/codegen-16B-multi/resolve/main/config.json''',
    '''Salesforce/codegen-16B-mono''': '''https://huggingface.co/Salesforce/codegen-16B-mono/resolve/main/config.json''',
}

class 		snake_case_   (							UpperCAmelCase_						):







	'''simple docstring'''
	__UpperCamelCase   =		'''codegen'''
	__UpperCamelCase   =		{
	    '''max_position_embeddings''': '''n_positions''',
	    '''hidden_size''': '''n_embd''',
	    '''num_attention_heads''': '''n_head''',
	    '''num_hidden_layers''': '''n_layer''',
	}




	def __init__(						self							:     Optional[int]					,      __lowerCamelCase							:     Optional[int]=50_400					,      __lowerCamelCase							:     Optional[Any]=2_048					,      __lowerCamelCase							:     List[str]=2_048					,      __lowerCamelCase							:     Any=4_096					,      __lowerCamelCase							:     int=28					,      __lowerCamelCase							:     Any=16					,      __lowerCamelCase							:     List[str]=64					,      __lowerCamelCase							:     Any=None					,      __lowerCamelCase							:     Tuple="gelu_new"					,      __lowerCamelCase							:     int=0.0					,      __lowerCamelCase							:     str=0.0					,      __lowerCamelCase							:     List[str]=0.0					,      __lowerCamelCase							:     str=1E-5					,      __lowerCamelCase							:     str=0.02					,      __lowerCamelCase							:     Dict=True					,      __lowerCamelCase							:     List[Any]=50_256					,      __lowerCamelCase							:     Optional[int]=50_256					,      __lowerCamelCase							:     str=False					,      **__lowerCamelCase							:     Optional[int]					,      )       -> int:

			'''simple docstring'''




			__lowercase											=     vocab_size
			__lowercase											=     n_ctx
			__lowercase											=     n_positions
			__lowercase											=     n_embd
			__lowercase											=     n_layer
			__lowercase											=     n_head
			__lowercase											=     n_inner
			__lowercase											=     rotary_dim
			__lowercase											=     activation_function
			__lowercase											=     resid_pdrop
			__lowercase											=     embd_pdrop
			__lowercase											=     attn_pdrop
			__lowercase											=     layer_norm_epsilon
			__lowercase											=     initializer_range
			__lowercase											=     use_cache

			__lowercase											=     bos_token_id
			__lowercase											=     eos_token_id

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





class 		snake_case_   (							UpperCAmelCase_						):







	'''simple docstring'''




	def __init__(						self							:     Optional[int]					,      __lowerCamelCase							:     PretrainedConfig					,      __lowerCamelCase							:     str = "default"					,      __lowerCamelCase							:     List[PatchingSpec] = None					,      __lowerCamelCase							:     bool = False					,      )       -> Optional[int]:

			'''simple docstring'''




			super().__init__(__lowerCamelCase					,      task=__lowerCamelCase					,      patching_specs=__lowerCamelCase					,      use_past=__lowerCamelCase   )
			if not getattr(self._config					,      'pad_token_id'					,      __lowerCamelCase   ):
					# TODO: how to do that better?
					__lowercase											=     0




	@property
	def    UpperCAmelCase			(						self							:     Dict   )       -> Mapping[str, Mapping[int, str]]:

			'''simple docstring'''




			__lowercase											=     OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}   )
			if self.use_past:
					self.fill_with_past_key_values_(__lowerCamelCase					,      direction='inputs'   )
					__lowercase											=     {0: 'batch', 1: 'past_sequence + sequence'}
			else:
					__lowercase											=     {0: 'batch', 1: 'sequence'}

			return common_inputs




	@property
	def    UpperCAmelCase			(						self							:     List[Any]   )       -> int:

			'''simple docstring'''




			return self._config.n_layer




	@property
	def    UpperCAmelCase			(						self							:     Any   )       -> int:

			'''simple docstring'''




			return self._config.n_head




	def    UpperCAmelCase			(						self							:     Tuple					,      __lowerCamelCase							:     PreTrainedTokenizer					,      __lowerCamelCase							:     int = -1					,      __lowerCamelCase							:     int = -1					,      __lowerCamelCase							:     bool = False					,      __lowerCamelCase							:     Optional[TensorType] = None					,      )       -> Mapping[str, Any]:

			'''simple docstring'''




			__lowercase											=     super(__lowerCamelCase					,      self   ).generate_dummy_inputs(
			    __lowerCamelCase					,      batch_size=__lowerCamelCase					,      seq_length=__lowerCamelCase					,      is_pair=__lowerCamelCase					,      framework=__lowerCamelCase   )

			# We need to order the input in the way they appears in the forward()
			__lowercase											=     OrderedDict({'input_ids': common_inputs['input_ids']}   )

			# Need to add the past_keys
			if self.use_past:
					if not is_torch_available():
							raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'   )
					else:
							import torch

							__lowercase						,      __lowercase											=     common_inputs['input_ids'].shape
							# Not using the same length for past_key_values
							__lowercase											=     seqlen + 2
							__lowercase											=     (
							    batch,
							    self.num_attention_heads,
							    past_key_values_length,
							    self._config.hidden_size // self.num_attention_heads,
							)
							__lowercase											=     [
							    (torch.zeros(__lowerCamelCase   ), torch.zeros(__lowerCamelCase   )) for _ in range(self.num_layers   )
							]

			__lowercase											=     common_inputs['attention_mask']
			if self.use_past:
					__lowercase											=     ordered_inputs['attention_mask'].dtype
					__lowercase											=     torch.cat(
					    [ordered_inputs['attention_mask'], torch.ones(__lowerCamelCase					,      __lowerCamelCase					,      dtype=__lowerCamelCase   )]					,      dim=1   )

			return ordered_inputs






	@property
	def    UpperCAmelCase			(						self							:     Optional[int]   )       -> int:

			'''simple docstring'''




			return 13











def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case						,   snake_case							)		->    int:
		# Return True if there is node that has not iterated.
		__lowercase											=     [False] * len(snake_case							)
		__lowercase											=     []
		queue.append(snake_case							)
		__lowercase											=     True

		while queue:
				__lowercase											=     queue.pop(0							)
				for ind in range(len(graph[u]							)							):
						if visited[ind] is False and graph[u][ind] > 0:
								queue.append(snake_case							)
								__lowercase											=     True
								__lowercase											=     u

		return visited[t]



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case							)		->    Dict:
		# This array is filled by BFS and to store path
		__lowercase											=     [-1] * (len(snake_case							))
		__lowercase											=     0
		while bfs(snake_case						,   snake_case						,   snake_case						,   snake_case							):
				__lowercase											=     float('Inf'							)
				__lowercase											=     sink

				while s != source:
						# Find the minimum value in select path
						__lowercase											=     min(snake_case						,   graph[parent[s]][s]							)
						__lowercase											=     parent[s]

				max_flow += path_flow
				__lowercase											=     sink

				while v != source:
						__lowercase											=     parent[v]
						graph[u][v] -= path_flow
						graph[v][u] += path_flow
						__lowercase											=     parent[v]
		return max_flow


SCREAMING_SNAKE_CASE_   :      Any			=   [
    [0, 16, 13, 0, 0, 0],
    [0, 0, 10, 12, 0, 0],
    [0, 4, 0, 0, 14, 0],
    [0, 0, 9, 0, 0, 20],
    [0, 0, 0, 7, 0, 4],
    [0, 0, 0, 0, 0, 0],
]

SCREAMING_SNAKE_CASE_	,SCREAMING_SNAKE_CASE_   :      Optional[Any]			=   0, 5


print(ford_fulkerson(graph, source, sink))




