

import json
import os
import subprocess
import sys
import warnings
from argparse import ArgumentParser
from contextlib import AbstractContextManager
from typing import Dict, List, Optional

import requests

from ..utils import logging
from . import BaseTransformersCLICommand


__snake_case							:     Tuple 						=	logging.get_logger(__name__)  # pylint: disable=invalid-name


__snake_case							:     Tuple 						=	"""lfs-multipart-upload"""



class    A__(a_    ):
						"""simple docstring"""






						@staticmethod
						def 	UpperCamelCase__			(  _lowercase  )      ->				str:
									a_		:		Any				    =				parser.add_parser(
									    """lfs-enable-largefiles"""       ,     help=(
									        """Deprecated: use `huggingface-cli` instead. Configure your repository to enable upload of files > 5GB."""
									    )       ,     )
									enable_parser.add_argument("""path"""       ,     type=_lowercase       ,     help="""Local path to repository you want to configure."""  )
									enable_parser.set_defaults(func=lambda _lowercase  : LfsEnableCommand(_lowercase  )  )

									a_		:		Dict				    =				parser.add_parser(
									    _lowercase       ,     help=(
									        """Deprecated: use `huggingface-cli` instead. """
									        """Command will get called by git-lfs, do not call it directly."""
									    )       ,     )
									upload_parser.set_defaults(func=lambda _lowercase  : LfsUploadCommand(_lowercase  )  )







class    A__:
						"""simple docstring"""






						def __init__(  self       ,     _lowercase  )      ->				List[Any]:
									a_		:		int				    =				args





						def 	UpperCamelCase__			(  self  )      ->				Optional[Any]:
									warnings.warn(
									    """Managing repositories through transformers-cli is deprecated. Please use `huggingface-cli` instead."""  )
									a_		:		Union[str, Any]				    =				os.path.abspath(self.args.path  )
									if not os.path.isdir(_lowercase  ):
												print("""This does not look like a valid git repo."""  )
												exit(1  )
									subprocess.run(
									    """git config lfs.customtransfer.multipart.path transformers-cli""".split()       ,     check=_lowercase       ,     cwd=_lowercase  )
									subprocess.run(
									    F'''git config lfs.customtransfer.multipart.args {LFS_MULTIPART_UPLOAD_COMMAND}'''.split()       ,     check=_lowercase       ,     cwd=_lowercase       ,     )
									print("""Local repo set up for largefiles"""  )







def 			_UpperCAmelCase							(		a__):

			'''simple docstring'''



			a_		:		List[Any]				    =				json.dumps(a__) + """\n"""
			sys.stdout.write(a__)
			sys.stdout.flush()


def 			_UpperCAmelCase							(		):

			'''simple docstring'''



			a_		:		List[str]				    =				json.loads(sys.stdin.readline().strip())

			if "terminate" in (msg.get("""type"""), msg.get("""event""")):
						# terminate message received
						return None

			if msg.get("""event""") not in ("download", "upload"):
						logger.critical("""Received unexpected message""")
						sys.exit(1)

			return msg



class    A__(a_    ):
						"""simple docstring"""






						def __init__(  self       ,     _lowercase       ,     _lowercase       ,     _lowercase  )      ->				Optional[Any]:
									a_		:		List[str]				    =				filepath
									a_		:		Any				    =				seek_from
									a_		:		Any				    =				read_limit
									a_		:		Optional[Any]				    =				0





						def __enter__(  self  )      ->				int:
									a_		:		str				    =				open(self.filepath       ,     """rb"""  )
									self.f.seek(self.seek_from  )
									return self





						def __len__(  self  )      ->				Optional[int]:
									a_		:		Union[str, Any]				    =				os.fstat(self.f.fileno()  ).st_size
									return min(self.read_limit       ,     total_length - self.seek_from  )





						def 	UpperCamelCase__			(  self       ,     _lowercase=-1  )      ->				List[Any]:
									if self.n_seen >= self.read_limit:
												return b""
									a_		:		Any				    =				self.read_limit - self.n_seen
									a_		:		Tuple				    =				self.f.read(remaining_amount if n < 0 else min(_lowercase       ,     _lowercase  )  )
									self.n_seen += len(_lowercase  )
									return data





						def __iter__(  self  )      ->				List[Any]:
									yield self.read(n=4 * 1_024 * 1_024  )





						def __exit__(  self       ,     *_lowercase  )      ->				Optional[Any]:
									self.f.close()







class    A__:
						"""simple docstring"""






						def __init__(  self       ,     _lowercase  )      ->				Union[str, Any]:
									a_		:		Optional[int]				    =				args





						def 	UpperCamelCase__			(  self  )      ->				Optional[int]:
									# Immediately after invoking a custom transfer process, git-lfs
									# sends initiation data to the process over stdin.
									# This tells the process useful information about the configuration.
									a_		:		Dict				    =				json.loads(sys.stdin.readline().strip()  )
									if not (init_msg.get("""event"""  ) == "init" and init_msg.get("""operation"""  ) == "upload"):
												write_msg({"""error""": {"""code""": 32, """message""": """Wrong lfs init operation"""}}  )
												sys.exit(1  )

									# The transfer process should use the information it needs from the
									# initiation structure, and also perform any one-off setup tasks it
									# needs to do. It should then respond on stdout with a simple empty
									# confirmation structure, as follows:
									write_msg({}  )

									# After the initiation exchange, git-lfs will send any number of
									# transfer requests to the stdin of the transfer process, in a serial sequence.
									while True:
												a_		:		Any				    =				read_msg()
												if msg is None:
															# When all transfers have been processed, git-lfs will send
															# a terminate event to the stdin of the transfer process.
															# On receiving this message the transfer process should
															# clean up and terminate. No response is expected.
															sys.exit(0  )

												a_		:		Union[str, Any]				    =				msg["""oid"""]
												a_		:		Any				    =				msg["""path"""]
												a_		:		List[str]				    =				msg["""action"""]["""href"""]
												a_		:		Tuple				    =				msg["""action"""]["""header"""]
												a_		:		Union[str, Any]				    =				int(header.pop("""chunk_size"""  )  )
												a_		:		List[str]				    =				list(header.values()  )

												a_		:		str				    =				[]
												for i, presigned_url in enumerate(_lowercase  ):
															with FileSlice(_lowercase       ,     seek_from=i * chunk_size       ,     read_limit=_lowercase  ) as data:
																		a_		:		Optional[Any]				    =				requests.put(_lowercase       ,     data=_lowercase  )
																		r.raise_for_status()
																		parts.append(
																		    {
																		        """etag""": r.headers.get("""etag"""  ),
																		        """partNumber""": i + 1,
																		    }  )
																		# In order to support progress reporting while data is uploading / downloading,
																		# the transfer process should post messages to stdout
																		write_msg(
																		    {
																		        """event""": """progress""",
																		        """oid""": oid,
																		        """bytesSoFar""": (i + 1) * chunk_size,
																		        """bytesSinceLast""": chunk_size,
																		    }  )
																		# Not precise but that's ok.

												a_		:		Optional[Any]				    =				requests.post(
												    _lowercase       ,     json={
												        """oid""": oid,
												        """parts""": parts,
												    }       ,     )
												r.raise_for_status()

												write_msg({"""event""": """complete""", """oid""": oid}  )





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    A__:
						"""simple docstring"""






						def __init__(  self       ,     _lowercase       ,     _lowercase=14       ,     _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=0.0_2       ,     )      ->				Dict:
									a_		:		List[str]				    =				parent
									a_		:		Tuple				    =				batch_size
									a_		:		int				    =				seq_length
									a_		:		Tuple				    =				is_training
									a_		:		List[Any]				    =				use_input_mask
									a_		:		Dict				    =				use_token_type_ids
									a_		:		List[Any]				    =				use_labels
									a_		:		Optional[int]				    =				vocab_size
									a_		:		Any				    =				hidden_size
									a_		:		List[Any]				    =				num_hidden_layers
									a_		:		List[str]				    =				num_attention_heads
									a_		:		List[str]				    =				intermediate_size
									a_		:		List[str]				    =				hidden_act
									a_		:		Union[str, Any]				    =				hidden_dropout_prob
									a_		:		Tuple				    =				attention_probs_dropout_prob
									a_		:		Union[str, Any]				    =				max_position_embeddings
									a_		:		Tuple				    =				initializer_range
									a_		:		Any				    =				None
									a_		:		str				    =				vocab_size - 1
									a_		:		Optional[Any]				    =				vocab_size - 1
									a_		:		List[Any]				    =				vocab_size - 1





						def 	UpperCamelCase__			(  self  )      ->				Optional[int]:
									a_		:		Dict				    =				ids_tensor([self.batch_size, self.seq_length]       ,     self.vocab_size  )

									a_		:		List[str]				    =				None
									if self.use_input_mask:
												a_		:		Dict				    =				random_attention_mask([self.batch_size, self.seq_length]  )

									a_		:		Optional[int]				    =				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=_lowercase       ,     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 	UpperCamelCase__			(  self  )      ->				int:
									a_		:		Any				    =				self.prepare_config_and_inputs()
									a_    ,			a_    ,			a_		:		List[str]				    =				config_and_inputs
									a_		:		Optional[Any]				    =				{"""input_ids""": input_ids, """attention_mask""": attention_mask}
									return config, inputs_dict





						def 	UpperCamelCase__			(  self  )      ->				Tuple:
									a_    ,			a_    ,			a_		:		Dict				    =				self.prepare_config_and_inputs()

									a_		:		List[str]				    =				floats_tensor([self.batch_size, self.seq_length, self.hidden_size]  )
									a_		:		Optional[int]				    =				ids_tensor([self.batch_size, self.seq_length]       ,     vocab_size=2  )

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





						def 	UpperCamelCase__			(  self       ,     _lowercase       ,     _lowercase       ,     _lowercase       ,     _lowercase  )      ->				Optional[Any]:
									a_		:		Dict				    =				20
									a_		:		Tuple				    =				model_class_name(_lowercase  )

									a_		:		Tuple				    =				model.init_cache(input_ids.shape[0]       ,     _lowercase  )
									a_		:		Any				    =				jnp.ones((input_ids.shape[0], max_decoder_length)       ,     dtype="""i4"""  )

									a_		:		str				    =				jnp.broadcast_to(
									    jnp.arange(input_ids.shape[-1] - 1  )[None, :]       ,     (input_ids.shape[0], input_ids.shape[-1] - 1)  )
									a_		:		int				    =				model(
									    input_ids[:, :-1]       ,     attention_mask=_lowercase       ,     past_key_values=_lowercase       ,     position_ids=_lowercase       ,     )

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

									a_		:		Dict				    =				model(_lowercase  )

									a_		:		Tuple				    =				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 	UpperCamelCase__			(  self       ,     _lowercase       ,     _lowercase       ,     _lowercase       ,     _lowercase  )      ->				int:
									a_		:		Any				    =				20
									a_		:		Optional[int]				    =				model_class_name(_lowercase  )

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

									a_		:		Optional[Any]				    =				model.init_cache(input_ids.shape[0]       ,     _lowercase  )
									a_		:		Any				    =				jnp.broadcast_to(
									    jnp.arange(input_ids.shape[-1] - 1  )[None, :]       ,     (input_ids.shape[0], input_ids.shape[-1] - 1)  )

									a_		:		Optional[Any]				    =				model(
									    input_ids[:, :-1]       ,     attention_mask=_lowercase       ,     past_key_values=_lowercase       ,     position_ids=_lowercase       ,     )
									a_		:		List[str]				    =				jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]       ,     dtype="""i4"""  )
									a_		:		Dict				    =				model(
									    input_ids[:, -1:]       ,     past_key_values=outputs_cache.past_key_values       ,     attention_mask=_lowercase       ,     position_ids=_lowercase       ,     )

									a_		:		str				    =				model(_lowercase       ,     attention_mask=_lowercase  )

									a_		:		List[str]				    =				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    A__(a_,       a_,       unittest.TestCase    ):
						"""simple docstring"""






						_A  :							List[str]   			=	(FlaxGPTaModel, FlaxGPTaLMHeadModel) if is_flax_available() else ()
						_A  :							Dict   			=	(FlaxGPTaLMHeadModel,) if is_flax_available() else ()





						def 	UpperCamelCase__			(  self  )      ->				Optional[Any]:
									a_		:		List[str]				    =				FlaxGPTaModelTester(self  )





						def 	UpperCamelCase__			(  self  )      ->				int:
									for model_class_name in self.all_model_classes:
												a_    ,			a_    ,			a_		:		Any				    =				self.model_tester.prepare_config_and_inputs()
												self.model_tester.check_use_cache_forward(_lowercase       ,     _lowercase       ,     _lowercase       ,     _lowercase  )





						def 	UpperCamelCase__			(  self  )      ->				int:
									for model_class_name in self.all_model_classes:
												a_    ,			a_    ,			a_		:		Optional[Any]				    =				self.model_tester.prepare_config_and_inputs()
												self.model_tester.check_use_cache_forward_with_attn_mask(
												    _lowercase       ,     _lowercase       ,     _lowercase       ,     _lowercase  )





						@slow
						def 	UpperCamelCase__			(  self  )      ->				Tuple:
									a_		:		str				    =				GPTaTokenizer.from_pretrained("""gpt2"""       ,     pad_token="""</s>"""       ,     padding_side="""left"""  )
									a_		:		Optional[int]				    =				tokenizer(["""Hello this is a long string""", """Hey"""]       ,     return_tensors="""np"""       ,     padding=_lowercase       ,     truncation=_lowercase  )

									a_		:		Any				    =				FlaxGPTaLMHeadModel.from_pretrained("""gpt2"""  )
									a_		:		List[Any]				    =				False
									a_		:		List[Any]				    =				model.config.eos_token_id

									a_		:		Tuple				    =				jax.jit(model.generate  )

									a_		:		Union[str, Any]				    =				jit_generate(inputs["""input_ids"""]       ,     attention_mask=inputs["""attention_mask"""]  ).sequences

									a_		:		Dict				    =				tokenizer.batch_decode(_lowercase       ,     skip_special_tokens=_lowercase  )

									a_		:		Tuple				    =				[
									    """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(_lowercase       ,     _lowercase  )





						@is_pt_flax_cross_test
						def 	UpperCamelCase__			(  self  )      ->				List[str]:
									a_    ,			a_		:		Optional[int]				    =				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
															a_		:		List[Any]				    =				self._prepare_for_class(_lowercase       ,     _lowercase  )
															a_		:		List[str]				    =				{k: torch.tensor(v.tolist()  ) for k, v in prepared_inputs_dict.items()}

															# load corresponding PyTorch class
															a_		:		List[str]				    =				model_class.__name__[4:]  # Skip the "Flax" at the beginning
															a_		:		List[str]				    =				getattr(_lowercase       ,     _lowercase  )

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

															a_		:		Optional[int]				    =				convert_pytorch_state_dict_to_flax(pt_model.state_dict()       ,     _lowercase  )
															a_		:		Union[str, Any]				    =				fx_state

															with torch.no_grad():
																		a_		:		str				    =				pt_model(**_lowercase  ).to_tuple()

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

															with tempfile.TemporaryDirectory() as tmpdirname:
																		pt_model.save_pretrained(_lowercase  )
																		a_		:		Dict				    =				model_class.from_pretrained(_lowercase       ,     from_pt=_lowercase  )

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





						@is_pt_flax_cross_test
						def 	UpperCamelCase__			(  self  )      ->				Union[str, Any]:
									a_    ,			a_		:		str				    =				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
															a_		:		int				    =				self._prepare_for_class(_lowercase       ,     _lowercase  )
															a_		:		Optional[Any]				    =				{k: torch.tensor(v.tolist()  ) for k, v in prepared_inputs_dict.items()}

															# load corresponding PyTorch class
															a_		:		List[str]				    =				model_class.__name__[4:]  # Skip the "Flax" at the beginning
															a_		:		Union[str, Any]				    =				getattr(_lowercase       ,     _lowercase  )

															a_		:		Tuple				    =				pt_model_class(_lowercase  ).eval()
															a_		:		Dict				    =				model_class(_lowercase       ,     dtype=jnp.floataa  )

															a_		:		Optional[Any]				    =				load_flax_weights_in_pytorch_model(_lowercase       ,     fx_model.params  )
															a_    ,			a_		:		Dict				    =				pt_inputs["""input_ids"""].shape
															a_		:		Any				    =				np.random.randint(0       ,     seq_length - 1       ,     size=(batch_size,)  )
															for batch_idx, start_index in enumerate(_lowercase  ):
																		a_		:		Optional[int]				    =				0
																		a_		:		Dict				    =				1
																		a_		:		Union[str, Any]				    =				0
																		a_		:		Tuple				    =				1

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

															with torch.no_grad():
																		a_		:		str				    =				pt_model(**_lowercase  ).to_tuple()

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

															with tempfile.TemporaryDirectory() as tmpdirname:
																		fx_model.save_pretrained(_lowercase  )
																		a_		:		Dict				    =				pt_model_class.from_pretrained(_lowercase       ,     from_flax=_lowercase  )

															with torch.no_grad():
																		a_		:		List[str]				    =				pt_model_loaded(**_lowercase  ).to_tuple()

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





						@slow
						def 	UpperCamelCase__			(  self  )      ->				Any:
									for model_class_name in self.all_model_classes:
												a_		:		List[str]				    =				model_class_name.from_pretrained("""gpt2"""       ,     from_pt=_lowercase  )
												a_		:		List[str]				    =				model(np.ones((1, 1)  )  )
												self.assertIsNotNone(_lowercase  )


