


"""simple docstring"""






import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaConfig, GPTaTokenizer, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
	import jax
	import jax.numpy as jnp

	from transformers.modeling_flax_pytorch_utils import (
	    convert_pytorch_state_dict_to_flax,
	    load_flax_weights_in_pytorch_model,
	)
	from transformers.models.gpta.modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel

if is_torch_available():
	import torch


class       lowerCamelCase_		:





				"""simple docstring"""







				def __init__(    self							,		UpperCAmelCase__							,		UpperCAmelCase__=14							,		UpperCAmelCase__=7							,		UpperCAmelCase__=True							,		UpperCAmelCase__=True							,		UpperCAmelCase__=False							,		UpperCAmelCase__=True							,		UpperCAmelCase__=99							,		UpperCAmelCase__=32							,		UpperCAmelCase__=5							,		UpperCAmelCase__=4							,		UpperCAmelCase__=37							,		UpperCAmelCase__="gelu"							,		UpperCAmelCase__=0.1							,		UpperCAmelCase__=0.1							,		UpperCAmelCase__=512							,		UpperCAmelCase__=0.02							,		):
								SCREAMING_SNAKE_CASE__  				=					parent
								SCREAMING_SNAKE_CASE__  				=					batch_size
								SCREAMING_SNAKE_CASE__  				=					seq_length
								SCREAMING_SNAKE_CASE__  				=					is_training
								SCREAMING_SNAKE_CASE__  				=					use_input_mask
								SCREAMING_SNAKE_CASE__  				=					use_token_type_ids
								SCREAMING_SNAKE_CASE__  				=					use_labels
								SCREAMING_SNAKE_CASE__  				=					vocab_size
								SCREAMING_SNAKE_CASE__  				=					hidden_size
								SCREAMING_SNAKE_CASE__  				=					num_hidden_layers
								SCREAMING_SNAKE_CASE__  				=					num_attention_heads
								SCREAMING_SNAKE_CASE__  				=					intermediate_size
								SCREAMING_SNAKE_CASE__  				=					hidden_act
								SCREAMING_SNAKE_CASE__  				=					hidden_dropout_prob
								SCREAMING_SNAKE_CASE__  				=					attention_probs_dropout_prob
								SCREAMING_SNAKE_CASE__  				=					max_position_embeddings
								SCREAMING_SNAKE_CASE__  				=					initializer_range
								SCREAMING_SNAKE_CASE__  				=					None
								SCREAMING_SNAKE_CASE__  				=					vocab_size - 1
								SCREAMING_SNAKE_CASE__  				=					vocab_size - 1
								SCREAMING_SNAKE_CASE__  				=					vocab_size - 1



				def 	lowerCAmelCase__		(    self  ):
								SCREAMING_SNAKE_CASE__  				=					ids_tensor([self.batch_size, self.seq_length]							,		self.vocab_size  )

								SCREAMING_SNAKE_CASE__  				=					None
								if self.use_input_mask:
												SCREAMING_SNAKE_CASE__  				=					random_attention_mask([self.batch_size, self.seq_length]  )

								SCREAMING_SNAKE_CASE__  				=					GPTaConfig(
								    vocab_size=self.vocab_size							,		n_embd=self.hidden_size							,		n_layer=self.num_hidden_layers							,		n_head=self.num_attention_heads							,		n_positions=self.max_position_embeddings							,		use_cache=UpperCAmelCase__							,		bos_token_id=self.bos_token_id							,		eos_token_id=self.eos_token_id							,		pad_token_id=self.pad_token_id							,		)

								return (config, input_ids, input_mask)



				def 	lowerCAmelCase__		(    self  ):
								SCREAMING_SNAKE_CASE__  				=					self.prepare_config_and_inputs()
								SCREAMING_SNAKE_CASE__				,   SCREAMING_SNAKE_CASE__				,   SCREAMING_SNAKE_CASE__  				=					config_and_inputs
								SCREAMING_SNAKE_CASE__  				=					{"input_ids": input_ids, "attention_mask": attention_mask}
								return config, inputs_dict



				def 	lowerCAmelCase__		(    self  ):
								SCREAMING_SNAKE_CASE__				,   SCREAMING_SNAKE_CASE__				,   SCREAMING_SNAKE_CASE__  				=					self.prepare_config_and_inputs()

								SCREAMING_SNAKE_CASE__  				=					floats_tensor([self.batch_size, self.seq_length, self.hidden_size]  )
								SCREAMING_SNAKE_CASE__  				=					ids_tensor([self.batch_size, self.seq_length]							,		vocab_size=2  )

								return (
								    config,
								    input_ids,
								    attention_mask,
								    encoder_hidden_states,
								    encoder_attention_mask,
								)



				def 	lowerCAmelCase__		(    self							,		UpperCAmelCase__							,		UpperCAmelCase__							,		UpperCAmelCase__							,		UpperCAmelCase__  ):
								SCREAMING_SNAKE_CASE__  				=					20
								SCREAMING_SNAKE_CASE__  				=					model_class_name(UpperCAmelCase__  )

								SCREAMING_SNAKE_CASE__  				=					model.init_cache(input_ids.shape[0]							,		UpperCAmelCase__  )
								SCREAMING_SNAKE_CASE__  				=					jnp.ones((input_ids.shape[0], max_decoder_length)							,		dtype="i4"  )

								SCREAMING_SNAKE_CASE__  				=					jnp.broadcast_to(
								    jnp.arange(input_ids.shape[-1] - 1  )[None, :]							,		(input_ids.shape[0], input_ids.shape[-1] - 1)  )
								SCREAMING_SNAKE_CASE__  				=					model(
								    input_ids[:, :-1]							,		attention_mask=UpperCAmelCase__							,		past_key_values=UpperCAmelCase__							,		position_ids=UpperCAmelCase__							,		)

								SCREAMING_SNAKE_CASE__  				=					jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]							,		dtype="i4"  )
								SCREAMING_SNAKE_CASE__  				=					model(
								    input_ids[:, -1:]							,		attention_mask=UpperCAmelCase__							,		past_key_values=outputs_cache.past_key_values							,		position_ids=UpperCAmelCase__							,		)

								SCREAMING_SNAKE_CASE__  				=					model(UpperCAmelCase__  )

								SCREAMING_SNAKE_CASE__  				=					np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])  )  )
								self.parent.assertTrue(diff < 1e-3							,		msg=f'''Max diff is {diff}'''  )






				def 	lowerCAmelCase__		(    self							,		UpperCAmelCase__							,		UpperCAmelCase__							,		UpperCAmelCase__							,		UpperCAmelCase__  ):
								SCREAMING_SNAKE_CASE__  				=					20
								SCREAMING_SNAKE_CASE__  				=					model_class_name(UpperCAmelCase__  )

								SCREAMING_SNAKE_CASE__  				=					jnp.concatenate(
								    [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])  )]							,		axis=-1							,		)

								SCREAMING_SNAKE_CASE__  				=					model.init_cache(input_ids.shape[0]							,		UpperCAmelCase__  )
								SCREAMING_SNAKE_CASE__  				=					jnp.broadcast_to(
								    jnp.arange(input_ids.shape[-1] - 1  )[None, :]							,		(input_ids.shape[0], input_ids.shape[-1] - 1)  )

								SCREAMING_SNAKE_CASE__  				=					model(
								    input_ids[:, :-1]							,		attention_mask=UpperCAmelCase__							,		past_key_values=UpperCAmelCase__							,		position_ids=UpperCAmelCase__							,		)
								SCREAMING_SNAKE_CASE__  				=					jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]							,		dtype="i4"  )
								SCREAMING_SNAKE_CASE__  				=					model(
								    input_ids[:, -1:]							,		past_key_values=outputs_cache.past_key_values							,		attention_mask=UpperCAmelCase__							,		position_ids=UpperCAmelCase__							,		)

								SCREAMING_SNAKE_CASE__  				=					model(UpperCAmelCase__							,		attention_mask=UpperCAmelCase__  )

								SCREAMING_SNAKE_CASE__  				=					np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])  )  )
								self.parent.assertTrue(diff < 1e-3							,		msg=f'''Max diff is {diff}'''  )


@require_flax
class       lowerCamelCase_		(      lowercase							,	lowercase							,	unittest.TestCase ):





				"""simple docstring"""







				_lowerCAmelCase   :   List[Any]   				=       (FlaxGPTaModel, FlaxGPTaLMHeadModel) if is_flax_available() else ()
				_lowerCAmelCase   :   Optional[Any]   				=       (FlaxGPTaLMHeadModel,) if is_flax_available() else ()



				def 	lowerCAmelCase__		(    self  ):
								SCREAMING_SNAKE_CASE__  				=					FlaxGPTaModelTester(self  )



				def 	lowerCAmelCase__		(    self  ):
								for model_class_name in self.all_model_classes:
												SCREAMING_SNAKE_CASE__				,   SCREAMING_SNAKE_CASE__				,   SCREAMING_SNAKE_CASE__  				=					self.model_tester.prepare_config_and_inputs()
												self.model_tester.check_use_cache_forward(UpperCAmelCase__							,		UpperCAmelCase__							,		UpperCAmelCase__							,		UpperCAmelCase__  )



				def 	lowerCAmelCase__		(    self  ):
								for model_class_name in self.all_model_classes:
												SCREAMING_SNAKE_CASE__				,   SCREAMING_SNAKE_CASE__				,   SCREAMING_SNAKE_CASE__  				=					self.model_tester.prepare_config_and_inputs()
												self.model_tester.check_use_cache_forward_with_attn_mask(
												    UpperCAmelCase__							,		UpperCAmelCase__							,		UpperCAmelCase__							,		UpperCAmelCase__  )



				@slow
				def 	lowerCAmelCase__		(    self  ):
								SCREAMING_SNAKE_CASE__  				=					GPTaTokenizer.from_pretrained("gpt2"							,		pad_token="</s>"							,		padding_side="left"  )
								SCREAMING_SNAKE_CASE__  				=					tokenizer(["Hello this is a long string", "Hey"]							,		return_tensors="np"							,		padding=UpperCAmelCase__							,		truncation=UpperCAmelCase__  )

								SCREAMING_SNAKE_CASE__  				=					FlaxGPTaLMHeadModel.from_pretrained("gpt2"  )
								SCREAMING_SNAKE_CASE__  				=					False
								SCREAMING_SNAKE_CASE__  				=					model.config.eos_token_id

								SCREAMING_SNAKE_CASE__  				=					jax.jit(model.generate  )

								SCREAMING_SNAKE_CASE__  				=					jit_generate(inputs["input_ids"]							,		attention_mask=inputs["attention_mask"]  ).sequences

								SCREAMING_SNAKE_CASE__  				=					tokenizer.batch_decode(UpperCAmelCase__							,		skip_special_tokens=UpperCAmelCase__  )

								SCREAMING_SNAKE_CASE__  				=					[
								    "Hello this is a long string of words. I'm going to start with the first one.\n",
								    "Hey, I'm not sure if I'm going to be able to do",
								]

								self.assertListEqual(UpperCAmelCase__							,		UpperCAmelCase__  )



				@is_pt_flax_cross_test
				def 	lowerCAmelCase__		(    self  ):
								SCREAMING_SNAKE_CASE__				,   SCREAMING_SNAKE_CASE__  				=					self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
												with self.subTest(model_class.__name__  ):
																# prepare inputs
																SCREAMING_SNAKE_CASE__  				=					self._prepare_for_class(UpperCAmelCase__							,		UpperCAmelCase__  )
																SCREAMING_SNAKE_CASE__  				=					{k: torch.tensor(v.tolist()  ) for k, v in prepared_inputs_dict.items()}

																# load corresponding PyTorch class
																SCREAMING_SNAKE_CASE__  				=					model_class.__name__[4:]  # Skip the "Flax" at the beginning
																SCREAMING_SNAKE_CASE__  				=					getattr(UpperCAmelCase__							,		UpperCAmelCase__  )

																SCREAMING_SNAKE_CASE__				,   SCREAMING_SNAKE_CASE__  				=					pt_inputs["input_ids"].shape
																SCREAMING_SNAKE_CASE__  				=					np.random.randint(0							,		seq_length - 1							,		size=(batch_size,)  )
																for batch_idx, start_index in enumerate(UpperCAmelCase__  ):
																				SCREAMING_SNAKE_CASE__  				=					0
																				SCREAMING_SNAKE_CASE__  				=					1
																				SCREAMING_SNAKE_CASE__  				=					0
																				SCREAMING_SNAKE_CASE__  				=					1
																SCREAMING_SNAKE_CASE__  				=					pt_model_class(UpperCAmelCase__  ).eval()
																SCREAMING_SNAKE_CASE__  				=					model_class(UpperCAmelCase__							,		dtype=jnp.floataa  )

																SCREAMING_SNAKE_CASE__  				=					convert_pytorch_state_dict_to_flax(pt_model.state_dict()							,		UpperCAmelCase__  )
																SCREAMING_SNAKE_CASE__  				=					fx_state

																with torch.no_grad():
																				SCREAMING_SNAKE_CASE__  				=					pt_model(**UpperCAmelCase__  ).to_tuple()

																SCREAMING_SNAKE_CASE__  				=					fx_model(**UpperCAmelCase__  ).to_tuple()
																self.assertEqual(len(UpperCAmelCase__  )							,		len(UpperCAmelCase__  )							,		"Output lengths differ between Flax and PyTorch"  )
																for fx_output, pt_output in zip(UpperCAmelCase__							,		UpperCAmelCase__  ):
																				self.assert_almost_equals(fx_output[:, -1]							,		pt_output[:, -1].numpy()							,		4e-2  )

																with tempfile.TemporaryDirectory() as tmpdirname:
																				pt_model.save_pretrained(UpperCAmelCase__  )
																				SCREAMING_SNAKE_CASE__  				=					model_class.from_pretrained(UpperCAmelCase__							,		from_pt=UpperCAmelCase__  )

																SCREAMING_SNAKE_CASE__  				=					fx_model_loaded(**UpperCAmelCase__  ).to_tuple()
																self.assertEqual(
																    len(UpperCAmelCase__  )							,		len(UpperCAmelCase__  )							,		"Output lengths differ between Flax and PyTorch"  )
																for fx_output_loaded, pt_output in zip(UpperCAmelCase__							,		UpperCAmelCase__  ):
																				self.assert_almost_equals(fx_output_loaded[:, -1]							,		pt_output[:, -1].numpy()							,		4e-2  )



				@is_pt_flax_cross_test
				def 	lowerCAmelCase__		(    self  ):
								SCREAMING_SNAKE_CASE__				,   SCREAMING_SNAKE_CASE__  				=					self.model_tester.prepare_config_and_inputs_for_common()
								for model_class in self.all_model_classes:
												with self.subTest(model_class.__name__  ):
																# prepare inputs
																SCREAMING_SNAKE_CASE__  				=					self._prepare_for_class(UpperCAmelCase__							,		UpperCAmelCase__  )
																SCREAMING_SNAKE_CASE__  				=					{k: torch.tensor(v.tolist()  ) for k, v in prepared_inputs_dict.items()}

																# load corresponding PyTorch class
																SCREAMING_SNAKE_CASE__  				=					model_class.__name__[4:]  # Skip the "Flax" at the beginning
																SCREAMING_SNAKE_CASE__  				=					getattr(UpperCAmelCase__							,		UpperCAmelCase__  )

																SCREAMING_SNAKE_CASE__  				=					pt_model_class(UpperCAmelCase__  ).eval()
																SCREAMING_SNAKE_CASE__  				=					model_class(UpperCAmelCase__							,		dtype=jnp.floataa  )

																SCREAMING_SNAKE_CASE__  				=					load_flax_weights_in_pytorch_model(UpperCAmelCase__							,		fx_model.params  )
																SCREAMING_SNAKE_CASE__				,   SCREAMING_SNAKE_CASE__  				=					pt_inputs["input_ids"].shape
																SCREAMING_SNAKE_CASE__  				=					np.random.randint(0							,		seq_length - 1							,		size=(batch_size,)  )
																for batch_idx, start_index in enumerate(UpperCAmelCase__  ):
																				SCREAMING_SNAKE_CASE__  				=					0
																				SCREAMING_SNAKE_CASE__  				=					1
																				SCREAMING_SNAKE_CASE__  				=					0
																				SCREAMING_SNAKE_CASE__  				=					1

																# make sure weights are tied in PyTorch
																pt_model.tie_weights()

																with torch.no_grad():
																				SCREAMING_SNAKE_CASE__  				=					pt_model(**UpperCAmelCase__  ).to_tuple()

																SCREAMING_SNAKE_CASE__  				=					fx_model(**UpperCAmelCase__  ).to_tuple()
																self.assertEqual(len(UpperCAmelCase__  )							,		len(UpperCAmelCase__  )							,		"Output lengths differ between Flax and PyTorch"  )
																for fx_output, pt_output in zip(UpperCAmelCase__							,		UpperCAmelCase__  ):
																				self.assert_almost_equals(fx_output[:, -1]							,		pt_output[:, -1].numpy()							,		4e-2  )

																with tempfile.TemporaryDirectory() as tmpdirname:
																				fx_model.save_pretrained(UpperCAmelCase__  )
																				SCREAMING_SNAKE_CASE__  				=					pt_model_class.from_pretrained(UpperCAmelCase__							,		from_flax=UpperCAmelCase__  )

																with torch.no_grad():
																				SCREAMING_SNAKE_CASE__  				=					pt_model_loaded(**UpperCAmelCase__  ).to_tuple()

																self.assertEqual(
																    len(UpperCAmelCase__  )							,		len(UpperCAmelCase__  )							,		"Output lengths differ between Flax and PyTorch"  )
																for fx_output, pt_output in zip(UpperCAmelCase__							,		UpperCAmelCase__  ):
																				self.assert_almost_equals(fx_output[:, -1]							,		pt_output[:, -1].numpy()							,		4e-2  )






				@slow
				def 	lowerCAmelCase__		(    self  ):
								for model_class_name in self.all_model_classes:
												SCREAMING_SNAKE_CASE__  				=					model_class_name.from_pretrained("gpt2"							,		from_pt=UpperCAmelCase__  )
												SCREAMING_SNAKE_CASE__  				=					model(np.ones((1, 1)  )  )
												self.assertIsNotNone(UpperCAmelCase__  )




"""simple docstring"""






from __future__ import annotations

import sys


class       lowerCamelCase_		:





				"""simple docstring"""







				def __init__(    self							,		UpperCAmelCase__							,		UpperCAmelCase__  ):
								SCREAMING_SNAKE_CASE__  				=					letter
								SCREAMING_SNAKE_CASE__  				=					freq
								SCREAMING_SNAKE_CASE__  				=					{}






				def __repr__(    self  ):
								return f'''{self.letter}:{self.freq}'''


class       lowerCamelCase_		:





				"""simple docstring"""







				def __init__(    self							,		UpperCAmelCase__							,		UpperCAmelCase__							,		UpperCAmelCase__  ):
								SCREAMING_SNAKE_CASE__  				=					freq
								SCREAMING_SNAKE_CASE__  				=					left
								SCREAMING_SNAKE_CASE__  				=					right


def __lowercase  (   lowerCamelCase_	:   str	):
				SCREAMING_SNAKE_CASE__  				=					{}
				with open(lowerCamelCase_	) as f:
								while True:
												SCREAMING_SNAKE_CASE__  				=					f.read(1	)
												if not c:
																break
												SCREAMING_SNAKE_CASE__  				=					chars[c] + 1 if c in chars else 1
				return sorted((Letter(lowerCamelCase_    ,       lowerCamelCase_	) for c, f in chars.items())    ,       key=lambda lowerCamelCase_	: x.freq	)


def __lowercase  (   lowerCamelCase_	:   list[Letter]	):
				SCREAMING_SNAKE_CASE__  				=					letters  # type: ignore
				while len(lowerCamelCase_	) > 1:
								SCREAMING_SNAKE_CASE__  				=					response.pop(0	)
								SCREAMING_SNAKE_CASE__  				=					response.pop(0	)
								SCREAMING_SNAKE_CASE__  				=					left.freq + right.freq
								SCREAMING_SNAKE_CASE__  				=					TreeNode(lowerCamelCase_    ,       lowerCamelCase_    ,       lowerCamelCase_	)
								response.append(lowerCamelCase_	)
								response.sort(key=lambda lowerCamelCase_	: x.freq	)
				return response[0]


def __lowercase  (   lowerCamelCase_	:   Letter | TreeNode    ,       lowerCamelCase_	:   str	):
				if isinstance(lowerCamelCase_    ,       lowerCamelCase_	):
								SCREAMING_SNAKE_CASE__  				=					bitstring
								return [root]
				SCREAMING_SNAKE_CASE__  				=					root  # type: ignore
				SCREAMING_SNAKE_CASE__  				=					[]
				letters += traverse_tree(treenode.left    ,       bitstring + "0"	)
				letters += traverse_tree(treenode.right    ,       bitstring + "1"	)
				return letters


def __lowercase  (   lowerCamelCase_	:   str	):
				SCREAMING_SNAKE_CASE__  				=					parse_file(lowerCamelCase_	)
				SCREAMING_SNAKE_CASE__  				=					build_tree(lowerCamelCase_	)
				SCREAMING_SNAKE_CASE__  				=					{
				    k: v for letter in traverse_tree(lowerCamelCase_    ,       ""	) for k, v in letter.bitstring.items()
				}
				print(F'''Huffman Coding  of {file_path}: '''	)
				with open(lowerCamelCase_	) as f:
								while True:
												SCREAMING_SNAKE_CASE__  				=					f.read(1	)
												if not c:
																break
												print(letters[c]    ,       end=" "	)
				print()


if __name__ == "__main__":
	# pass the file path to the huffman function
	huffman(sys.argv[1])
