# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to taking a full-size model, reducing to the minimum its layers and
# emb dimensions, but keeping the full vocab + merges files, leading to ~3MB in total for all files.
# The latter is done by `fsmt-make-super-tiny-model.py`.
#
# It will be used then as "stas/tiny-wmt19-en-ru"

from pathlib import Path
import json
import tempfile

from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES

_lowerCamelCase       : Union[str, Any]      					=		"tiny-wmt19-en-ru"

# Build

# borrowed from a test
_lowerCamelCase       : List[str]      					=		[
    "l",
    "o",
    "w",
    "e",
    "r",
    "s",
    "t",
    "i",
    "d",
    "n",
    "w</w>",
    "r</w>",
    "t</w>",
    "lo",
    "low",
    "er</w>",
    "low</w>",
    "lowest</w>",
    "newer</w>",
    "wider</w>",
    "<unk>",
]
_lowerCamelCase       : List[Any]      					=		dict(zip(vocab, range(len(vocab))))
_lowerCamelCase       : List[Any]      					=		["l o 123", "lo w 1456", "e r</w> 1789", ""]

with tempfile.TemporaryDirectory() as tmpdirname:
							_lowerCamelCase       : Union[str, Any]      					=		Path(tmpdirname)
							_lowerCamelCase       : Tuple      					=		build_dir / VOCAB_FILES_NAMES["src_vocab_file"]
							_lowerCamelCase       : List[str]      					=		build_dir / VOCAB_FILES_NAMES["tgt_vocab_file"]
							_lowerCamelCase       : List[str]      					=		build_dir / VOCAB_FILES_NAMES["merges_file"]
							with open(src_vocab_file, '''w''') as fp:
														fp.write(json.dumps(vocab_tokens))
							with open(tgt_vocab_file, '''w''') as fp:
														fp.write(json.dumps(vocab_tokens))
							with open(merges_file, '''w''') as fp:
														fp.write('''\n'''.join(merges))

							_lowerCamelCase       : Tuple      					=		FSMTTokenizer(
							    langs=['''en''', '''ru'''],
							    src_vocab_size=len(vocab),
							    tgt_vocab_size=len(vocab),
							    src_vocab_file=src_vocab_file,
							    tgt_vocab_file=tgt_vocab_file,
							    merges_file=merges_file,
							)

_lowerCamelCase       : List[Any]      					=		FSMTConfig(
    langs=['''ru''', '''en'''],
    src_vocab_size=10_00,
    tgt_vocab_size=10_00,
    d_model=4,
    encoder_layers=1,
    decoder_layers=1,
    encoder_ffn_dim=4,
    decoder_ffn_dim=4,
    encoder_attention_heads=1,
    decoder_attention_heads=1,
)

_lowerCamelCase       : List[Any]      					=		FSMTForConditionalGeneration(config)
print(f"""num of params {tiny_model.num_parameters()}""")

# Test
_lowerCamelCase       : str      					=		tokenizer(['''Making tiny model'''], return_tensors='''pt''')
_lowerCamelCase       : str      					=		tiny_model(**batch)

print('''test output:''', len(outputs.logits[0]))

# Save
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(f"""Generated {mname_tiny}""")

# Upload
# transformers-cli upload tiny-wmt19-en-ru










import heapq




def 		lowerCAmelCase_ (						lowerCamelCase ):
				__magic_name__						:						list[list]										=[]

				# for each node and his adjacency list add them and the rank of the node to queue
				# using heapq module the queue will be filled like a Priority Queue
				# heapq works with a min priority queue, so I used -1*len(v) to build it
				for key, value in graph.items():
								# O(log(n))
								heapq.heappush(lowerCamelCase						,  [-1 * len(lowerCamelCase ), (key, value)] )

				# chosen_vertices = set of chosen vertices
				__magic_name__						:						Tuple										=set()

				# while queue isn't empty and there are still edges
				#   (queue[0][0] is the rank of the node with max rank)
				while queue and queue[0][0] != 0:
								# extract vertex with max rank from queue and add it to chosen_vertices
								__magic_name__						:						Tuple										=heapq.heappop(lowerCamelCase )[1][0]
								chosen_vertices.add(lowerCamelCase )

								# Remove all arcs adjacent to argmax
								for elem in queue:
												# if v haven't adjacent node, skip
												if elem[0] == 0:
																continue
												# if argmax is reachable from elem
												# remove argmax from elem's adjacent list and update his rank
												if argmax in elem[1][1]:
																__magic_name__						:						Tuple										=elem[1][1].index(lowerCamelCase )
																del elem[1][1][index]
																elem[0] += 1
        # re-order the queue
								heapq.heapify(lowerCamelCase )
				return chosen_vertices


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							UpperCAmelCase_		:					Optional[int]          =  {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
							print(F"""Minimum vertex cover:\n{greedy_min_vertex_cover(graph)}""")




