





"""simple docstring"""




from __future__ import annotations

import copy
import os
import tempfile
from importlib import import_module
from math import isnan

from transformers import is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import _tf_gpu_memory_limit, require_tf, slow

from ..test_modeling_tf_common import ids_tensor


if is_tf_available():
						import numpy as np
						import tensorflow as tf

						from transformers import (
						    TF_MODEL_FOR_CAUSAL_LM_MAPPING,
						    TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
						    TF_MODEL_FOR_MASKED_LM_MAPPING,
						    TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
						    TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
						    TF_MODEL_FOR_PRETRAINING_MAPPING,
						    TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
						    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
						    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
						    TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
						    TFSharedEmbeddings,
						)

						if _tf_gpu_memory_limit is not None:
												UpperCamelCase_     =tf.config.list_physical_devices("""GPU""")
												for gpu in gpus:
																		# Restrict TensorFlow to only allocate x GB of memory on the GPUs
																		try:
																								tf.config.set_logical_device_configuration(
																								    gpu, [tf.config.LogicalDeviceConfiguration(memory_limit=_tf_gpu_memory_limit)]
																								)
																								UpperCamelCase_     =tf.config.list_logical_devices("""GPU""")
																								print("""Logical GPUs""", logical_gpus)
																		except RuntimeError as e:
																								# Virtual devices must be set before GPUs have been initialized
																								print(e)




@require_tf
class 				_a		:
				UpperCamelCase			=					None
				UpperCamelCase			=					()
				UpperCamelCase			=					()
				UpperCamelCase			=					True
				UpperCamelCase			=					True
				UpperCamelCase			=					True
				UpperCamelCase			=					False






				def        snake_case			(				self	:					List[str],				lowerCAmelCase__	:					Any,				lowerCAmelCase__	:					int,				lowerCAmelCase__	:					Dict=False							)			->   dict:




										'''simple docstring'''



										_UpperCamelCase    :      List[Any]						     =					copy.deepcopy(lowerCAmelCase__							)

										if model_class in get_values(lowerCAmelCase__							):
																_UpperCamelCase    :      Any						     =					{
																    k: tf.tile(tf.expand_dims(lowerCAmelCase__,				1							),				(1, self.model_tester.num_choices) + (1,) * (v.ndim - 1)							)
																    if isinstance(lowerCAmelCase__,				tf.Tensor							) and v.ndim > 0
																    else v
																    for k, v in inputs_dict.items()
																}

										if return_labels:
																if model_class in get_values(lowerCAmelCase__							):
																						_UpperCamelCase    :      int						     =					tf.ones(self.model_tester.batch_size,				dtype=tf.intaa							)
																elif model_class in get_values(lowerCAmelCase__							):
																						_UpperCamelCase    :      Dict						     =					tf.zeros(self.model_tester.batch_size,				dtype=tf.intaa							)
																						_UpperCamelCase    :      Any						     =					tf.zeros(self.model_tester.batch_size,				dtype=tf.intaa							)
																elif model_class in [
																    *get_values(lowerCAmelCase__							),
																    *get_values(lowerCAmelCase__							),
																]:
																						_UpperCamelCase    :      Dict						     =					tf.zeros(self.model_tester.batch_size,				dtype=tf.intaa							)
																elif model_class in get_values(lowerCAmelCase__							):
																						_UpperCamelCase    :      int						     =					tf.zeros(self.model_tester.batch_size,				dtype=tf.intaa							)
																elif model_class in [
																    *get_values(lowerCAmelCase__							),
																    *get_values(lowerCAmelCase__							),
																    *get_values(lowerCAmelCase__							),
																    *get_values(lowerCAmelCase__							),
																    *get_values(lowerCAmelCase__							),
																]:
																						_UpperCamelCase    :      Union[str, Any]						     =					tf.zeros(
																						    (self.model_tester.batch_size, self.model_tester.seq_length),				dtype=tf.intaa							)
										return inputs_dict






				@slow
				def        snake_case			(				self	:					str							)			->   Optional[Any]:




										'''simple docstring'''



										_UpperCamelCase     ,							_UpperCamelCase    :      int						     =					self.model_tester.prepare_config_and_inputs_for_common()
										for model_class in self.all_model_classes[:2]:
																_UpperCamelCase    :      List[Any]						     =					self._prepare_for_class(lowerCAmelCase__,				lowerCAmelCase__							)
																_UpperCamelCase    :      List[Any]						     =					model_class(lowerCAmelCase__							)

																@tf.function
																def run_in_graph_mode():
																						return model(lowerCAmelCase__							)

																_UpperCamelCase    :      Union[str, Any]						     =					run_in_graph_mode()
																self.assertIsNotNone(lowerCAmelCase__							)






				@slow
				def        snake_case			(				self	:					List[str]							)			->   List[Any]:




										'''simple docstring'''



										_UpperCamelCase     ,							_UpperCamelCase    :      str						     =					self.model_tester.prepare_config_and_inputs_for_common()
										for model_class in self.all_model_classes[:2]:
																_UpperCamelCase    :      Any						     =					self._prepare_for_class(lowerCAmelCase__,				lowerCAmelCase__							)
																_UpperCamelCase    :      List[str]						     =					model_class(lowerCAmelCase__							)

																@tf.function(experimental_compile=lowerCAmelCase__							)
																def run_in_graph_mode():
																						return model(lowerCAmelCase__							)

																_UpperCamelCase    :      Any						     =					run_in_graph_mode()
																self.assertIsNotNone(lowerCAmelCase__							)






				@slow
				def        snake_case			(				self	:					Optional[int]							)			->   Any:




										'''simple docstring'''



										_UpperCamelCase     ,							_UpperCamelCase    :      Union[str, Any]						     =					self.model_tester.prepare_config_and_inputs_for_common()
										for model_class in self.all_model_classes[:2]:
																_UpperCamelCase    :      Tuple						     =					model_class(lowerCAmelCase__							)
																if getattr(lowerCAmelCase__,				'''hf_compute_loss''',				lowerCAmelCase__							):
																						# Test that model correctly compute the loss with kwargs
																						_UpperCamelCase    :      Optional[Any]						     =					self._prepare_for_class(inputs_dict.copy(),				lowerCAmelCase__,				return_labels=lowerCAmelCase__							)
																						# Is there a better way to remove these decoder inputs?
																						_UpperCamelCase    :      Any						     =					{
																						    key: val
																						    for key, val in prepared_for_class.items()
																						    if key not in ('''head_mask''', '''decoder_head_mask''', '''cross_attn_head_mask''', '''decoder_input_ids''')
																						}

																						_UpperCamelCase    :      Dict						     =					{
																						    '''labels''',
																						    '''label''',
																						    '''label_ids''',
																						    '''start_positions''',
																						    '''start_position''',
																						    '''end_positions''',
																						    '''end_position''',
																						    '''next_sentence_label''',
																						}
																						_UpperCamelCase    :      Tuple						     =					possible_label_cols.intersection(set(lowerCAmelCase__							)							)
																						self.assertGreater(len(lowerCAmelCase__							),				0,				msg='''No matching label names found!'''							)
																						_UpperCamelCase    :      Tuple						     =					{key: val for key, val in prepared_for_class.items() if key in label_names}
																						_UpperCamelCase    :      Dict						     =					{key: val for key, val in prepared_for_class.items() if key not in label_names}
																						self.assertGreater(len(lowerCAmelCase__							),				0							)

																						# Make sure it works with XLA!
																						model.compile(optimizer=tf.keras.optimizers.SGD(0.0							),				jit_compile=lowerCAmelCase__							)
																						# Make sure the model fits without crashing regardless of where we pass the labels
																						_UpperCamelCase    :      Optional[int]						     =					model.fit(
																						    lowerCAmelCase__,				validation_data=lowerCAmelCase__,				steps_per_epoch=1,				validation_steps=1,				shuffle=lowerCAmelCase__,				verbose=0,				)
																						_UpperCamelCase    :      Tuple						     =					history.history['''loss'''][0]
																						self.assertTrue(not isnan(lowerCAmelCase__							)							)
																						_UpperCamelCase    :      str						     =					history.history['''val_loss'''][0]
																						self.assertTrue(not isnan(lowerCAmelCase__							)							)

																						# Now test it with separate labels, to make sure that path works in XLA too.
																						_UpperCamelCase    :      Optional[int]						     =					model_class(lowerCAmelCase__							)
																						model.compile(optimizer=tf.keras.optimizers.SGD(0.0							),				jit_compile=lowerCAmelCase__							)
																						_UpperCamelCase    :      Optional[Any]						     =					model.fit(
																						    lowerCAmelCase__,				lowerCAmelCase__,				validation_data=(inputs_minus_labels, labels),				steps_per_epoch=1,				validation_steps=1,				shuffle=lowerCAmelCase__,				verbose=0,				)

																						_UpperCamelCase    :      Union[str, Any]						     =					history.history['''loss'''][0]
																						self.assertTrue(not isnan(lowerCAmelCase__							)							)
																						_UpperCamelCase    :      List[Any]						     =					history.history['''val_loss'''][0]
																						self.assertTrue(not isnan(lowerCAmelCase__							)							)






				@slow
				def        snake_case			(				self	:					int							)			->   str:




										'''simple docstring'''



										_UpperCamelCase     ,							_UpperCamelCase    :      Optional[Any]						     =					self.model_tester.prepare_config_and_inputs_for_common()
										_UpperCamelCase    :      List[Any]						     =					True
										_UpperCamelCase    :      Optional[int]						     =					True

										if hasattr(lowerCAmelCase__,				'''use_cache'''							):
																_UpperCamelCase    :      Optional[Any]						     =					True

										_UpperCamelCase    :      List[Any]						     =					getattr(self.model_tester,				'''encoder_seq_length''',				self.model_tester.seq_length							)
										_UpperCamelCase    :      Dict						     =					getattr(self.model_tester,				'''key_length''',				lowerCAmelCase__							)

										for model_class in self.all_model_classes[:2]:
																_UpperCamelCase    :      Dict						     =					self._prepare_for_class(lowerCAmelCase__,				lowerCAmelCase__							)
																_UpperCamelCase    :      int						     =					model_class(lowerCAmelCase__							)
																model.build()
																_UpperCamelCase    :      Tuple						     =					len(model(lowerCAmelCase__							)							)

																for key in list(class_inputs_dict.keys()							):
																						# Remove keys not in the serving signature, as the SavedModel will not be compiled to deal with them
																						if key not in model.input_signature:
																												del class_inputs_dict[key]
																						# Check it's a tensor, in case the inputs dict has some bools in it too
																						elif isinstance(class_inputs_dict[key],				tf.Tensor							) and class_inputs_dict[key].dtype.is_integer:
																												_UpperCamelCase    :      Tuple						     =					tf.cast(class_inputs_dict[key],				tf.intaa							)

																if set(class_inputs_dict.keys()							) != set(model.input_signature.keys()							):
																						continue  # Some models have inputs that the preparation functions don't create, we skip those

																with tempfile.TemporaryDirectory() as tmpdirname:
																						model.save_pretrained(lowerCAmelCase__,				saved_model=lowerCAmelCase__							)
																						_UpperCamelCase    :      List[str]						     =					os.path.join(lowerCAmelCase__,				'''saved_model''',				'''1'''							)
																						_UpperCamelCase    :      List[str]						     =					tf.keras.models.load_model(lowerCAmelCase__							)
																						_UpperCamelCase    :      Any						     =					model(lowerCAmelCase__							)

																						if self.is_encoder_decoder:
																												_UpperCamelCase    :      str						     =					outputs['''encoder_hidden_states''']
																												_UpperCamelCase    :      Any						     =					outputs['''encoder_attentions''']
																						else:
																												_UpperCamelCase    :      Dict						     =					outputs['''hidden_states''']
																												_UpperCamelCase    :      Optional[int]						     =					outputs['''attentions''']

																						self.assertEqual(len(lowerCAmelCase__							),				lowerCAmelCase__							)

																						_UpperCamelCase    :      Tuple						     =					getattr(
																						    self.model_tester,				'''expected_num_hidden_layers''',				self.model_tester.num_hidden_layers + 1							)

																						self.assertEqual(len(lowerCAmelCase__							),				lowerCAmelCase__							)
																						self.assertListEqual(
																						    list(output_hidden_states[0].shape[-2:]							),				[self.model_tester.seq_length, self.model_tester.hidden_size],				)

																						self.assertEqual(len(lowerCAmelCase__							),				self.model_tester.num_hidden_layers							)
																						self.assertListEqual(
																						    list(output_attentions[0].shape[-3:]							),				[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length],				)






				@slow
				def        snake_case			(				self	:					Any							)			->   Any:




										'''simple docstring'''



										tf.keras.mixed_precision.set_global_policy('''mixed_float16'''							)

										# try/finally block to ensure subsequent tests run in float32
										try:
																_UpperCamelCase     ,							_UpperCamelCase    :      Union[str, Any]						     =					self.model_tester.prepare_config_and_inputs_for_common()
																for model_class in self.all_model_classes[:2]:
																						_UpperCamelCase    :      int						     =					self._prepare_for_class(lowerCAmelCase__,				lowerCAmelCase__							)
																						_UpperCamelCase    :      List[Any]						     =					model_class(lowerCAmelCase__							)
																						_UpperCamelCase    :      Optional[int]						     =					model(lowerCAmelCase__							)

																						self.assertIsNotNone(lowerCAmelCase__							)
										finally:
																tf.keras.mixed_precision.set_global_policy('''float32'''							)






				@slow
				def        snake_case			(				self	:					str							)			->   int:




										'''simple docstring'''



										_UpperCamelCase     ,							_UpperCamelCase    :      str						     =					self.model_tester.prepare_config_and_inputs_for_common()
										# head_mask and decoder_head_mask has different shapes than other input args
										if "head_mask" in inputs_dict:
																del inputs_dict["head_mask"]
										if "decoder_head_mask" in inputs_dict:
																del inputs_dict["decoder_head_mask"]
										if "cross_attn_head_mask" in inputs_dict:
																del inputs_dict["cross_attn_head_mask"]
										_UpperCamelCase    :      int						     =					{
										    module_member
										    for model_class in self.all_model_classes
										    for module in (import_module(model_class.__module__							),)
										    for module_member_name in dir(lowerCAmelCase__							)
										    if module_member_name.endswith('''MainLayer'''							)
										    for module_member in (getattr(lowerCAmelCase__,				lowerCAmelCase__							),)
										    if isinstance(lowerCAmelCase__,				lowerCAmelCase__							)
										    and tf.keras.layers.Layer in module_member.__bases__
										    and getattr(lowerCAmelCase__,				'''_keras_serializable''',				lowerCAmelCase__							)
										}

										for main_layer_class in tf_main_layer_classes:
																# T5MainLayer needs an embed_tokens parameter when called without the inputs_embeds parameter
																if "T5" in main_layer_class.__name__:
																						# Take the same values than in TFT5ModelTester for this shared layer
																						_UpperCamelCase    :      List[str]						     =					TFSharedEmbeddings(self.model_tester.vocab_size,				self.model_tester.hidden_size,				name='''shared'''							)
																						_UpperCamelCase    :      Any						     =					False
																						_UpperCamelCase    :      int						     =					main_layer_class(lowerCAmelCase__,				embed_tokens=lowerCAmelCase__							)
																else:
																						_UpperCamelCase    :      Tuple						     =					main_layer_class(lowerCAmelCase__							)

																_UpperCamelCase    :      Optional[int]						     =					{
																    name: tf.keras.Input(tensor.shape[1:],				dtype=tensor.dtype							) for name, tensor in inputs_dict.items()
																}

																if hasattr(self.model_tester,				'''num_labels'''							):
																						_UpperCamelCase    :      Optional[Any]						     =					self.model_tester.num_labels
																else:
																						_UpperCamelCase    :      Optional[int]						     =					2

																_UpperCamelCase    :      List[str]						     =					tf.data.Dataset.from_tensor_slices(
																    (inputs_dict, np.ones((self.model_tester.batch_size, self.model_tester.seq_length, num_labels, 1)							))							).batch(1							)

																_UpperCamelCase    :      Optional[Any]						     =					main_layer(lowerCAmelCase__							)[0]
																_UpperCamelCase    :      Dict						     =					tf.keras.layers.Dense(lowerCAmelCase__,				activation='''softmax''',				name='''outputs'''							)(lowerCAmelCase__							)
																_UpperCamelCase    :      Dict						     =					tf.keras.models.Model(inputs=lowerCAmelCase__,				outputs=[outputs]							)

																model.compile(loss='''binary_crossentropy''',				optimizer='''adam''',				metrics=['''binary_accuracy''']							)
																model.fit(lowerCAmelCase__,				epochs=1							)

																with tempfile.TemporaryDirectory() as tmpdirname:
																						_UpperCamelCase    :      Optional[int]						     =					os.path.join(lowerCAmelCase__,				'''keras_model.h5'''							)
																						model.save(lowerCAmelCase__							)
																						if "T5" in main_layer_class.__name__:
																												_UpperCamelCase    :      List[str]						     =					tf.keras.models.load_model(
																												    lowerCAmelCase__,				custom_objects={
																												        main_layer_class.__name__: main_layer_class,
																												        '''TFSharedEmbeddings''': TFSharedEmbeddings,
																												    },				)
																						else:
																												_UpperCamelCase    :      Union[str, Any]						     =					tf.keras.models.load_model(
																												    lowerCAmelCase__,				custom_objects={main_layer_class.__name__: main_layer_class}							)
																						assert isinstance(lowerCAmelCase__,				tf.keras.Model							)
																						model(lowerCAmelCase__							)






				@slow
				def        snake_case			(				self	:					str							)			->   Union[str, Any]:




										'''simple docstring'''



										_UpperCamelCase     ,							_UpperCamelCase    :      Dict						     =					self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes[:2]:
																_UpperCamelCase    :      Dict						     =					model_class(lowerCAmelCase__							)

																_UpperCamelCase    :      Optional[int]						     =					copy.deepcopy(lowerCAmelCase__							)

																if not self.is_encoder_decoder:
																						_UpperCamelCase    :      Union[str, Any]						     =					inputs['''input_ids''']
																						del inputs["input_ids"]
																else:
																						_UpperCamelCase    :      Tuple						     =					inputs['''input_ids''']
																						_UpperCamelCase    :      Optional[Any]						     =					inputs.get('''decoder_input_ids''',				lowerCAmelCase__							)
																						del inputs["input_ids"]
																						inputs.pop('''decoder_input_ids''',				lowerCAmelCase__							)

																if not self.is_encoder_decoder:
																						_UpperCamelCase    :      List[str]						     =					model.get_input_embeddings()(lowerCAmelCase__							)
																else:
																						_UpperCamelCase    :      List[Any]						     =					model.get_input_embeddings()(lowerCAmelCase__							)
																						_UpperCamelCase    :      List[Any]						     =					model.get_input_embeddings()(lowerCAmelCase__							)

																_UpperCamelCase    :      str						     =					self._prepare_for_class(lowerCAmelCase__,				lowerCAmelCase__							)

																@tf.function
																def run_in_graph_mode():
																						return model(lowerCAmelCase__							)

																_UpperCamelCase    :      str						     =					run_in_graph_mode()
																self.assertIsNotNone(lowerCAmelCase__							)






				def        snake_case			(				self	:					Tuple,				lowerCAmelCase__	:					List[str],				lowerCAmelCase__	:					Optional[Any]							)			->   Optional[int]:




										'''simple docstring'''



										_UpperCamelCase    :      List[str]						     =					[]
										if model.config.bos_token_id is not None:
																special_tokens.append(model.config.bos_token_id							)
										if model.config.pad_token_id is not None:
																special_tokens.append(model.config.pad_token_id							)
										if model.config.eos_token_id is not None:
																special_tokens.append(model.config.eos_token_id							)

										# create random bad tokens that are not special tokens
										_UpperCamelCase    :      int						     =					[]
										while len(lowerCAmelCase__							) < num_bad_tokens:
																_UpperCamelCase    :      List[Any]						     =					tf.squeeze(ids_tensor((1, 1),				self.model_tester.vocab_size							),				0							).numpy()[0]
																if token not in special_tokens:
																						bad_tokens.append(lowerCAmelCase__							)
										return bad_tokens






				def        snake_case			(				self	:					Optional[int],				lowerCAmelCase__	:					Optional[Any]							)			->   int:




										'''simple docstring'''



										for token_id in output_ids[0].numpy().tolist():
																self.assertGreaterEqual(lowerCAmelCase__,				0							)
																self.assertLess(lowerCAmelCase__,				self.model_tester.vocab_size							)






				def        snake_case			(				self	:					Tuple,				lowerCAmelCase__	:					List[str],				lowerCAmelCase__	:					Tuple							)			->   Optional[Any]:




										'''simple docstring'''



										for bad_word_ids in bad_words_ids:
																# for all slices in batch
																for generated_ids_slice in generated_ids:
																						# for all word idx
																						for i in range(len(lowerCAmelCase__							),				len(lowerCAmelCase__							)							):
																												# if tokens match
																												if generated_ids_slice[i - len(lowerCAmelCase__							) : i] == bad_word_ids:
																																		return True
										return False










"""simple docstring"""




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


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


UpperCamelCase_     ="""lfs-multipart-upload"""




class 				_a		(   _lowerCAmelCase       ):






				@staticmethod
				def        snake_case			(				lowerCAmelCase__	:					ArgumentParser							)			->   Any:




										'''simple docstring'''



										_UpperCamelCase    :      Optional[int]						     =					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=lowerCAmelCase__,				help='''Local path to repository you want to configure.'''							)
										enable_parser.set_defaults(func=lambda lowerCAmelCase__							: LfsEnableCommand(lowerCAmelCase__							)							)

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




class 				_a		:






				def __init__(				self	:					Union[str, Any],				lowerCAmelCase__	:					str							)			->   Dict:




										'''simple docstring'''



										_UpperCamelCase    :      Optional[Any]						     =					args






				def        snake_case			(				self	:					str							)			->   str:




										'''simple docstring'''



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





def  a_       (							_lowercase     ):
						_UpperCamelCase    :      Tuple						     =					json.dumps(_lowercase     ) + '''\n'''
						sys.stdout.write(_lowercase     )
						sys.stdout.flush()





def  a_       (							):
						_UpperCamelCase    :      Optional[Any]						     =					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		(   _lowerCAmelCase       ):






				def __init__(				self	:					Tuple,				lowerCAmelCase__	:					str,				lowerCAmelCase__	:					int,				lowerCAmelCase__	:					int							)			->   Union[str, Any]:




										'''simple docstring'''



										_UpperCamelCase    :      int						     =					filepath
										_UpperCamelCase    :      Tuple						     =					seek_from
										_UpperCamelCase    :      Optional[int]						     =					read_limit
										_UpperCamelCase    :      List[str]						     =					0






				def __enter__(				self	:					Union[str, Any]							)			->   Union[str, Any]:




										'''simple docstring'''



										_UpperCamelCase    :      Optional[Any]						     =					open(self.filepath,				'''rb'''							)
										self.f.seek(self.seek_from							)
										return self






				def __len__(				self	:					str							)			->   str:




										'''simple docstring'''



										_UpperCamelCase    :      List[str]						     =					os.fstat(self.f.fileno()							).st_size
										return min(self.read_limit,				total_length - self.seek_from							)






				def        snake_case			(				self	:					Dict,				lowerCAmelCase__	:					List[str]=-1							)			->   int:




										'''simple docstring'''



										if self.n_seen >= self.read_limit:
																return b""
										_UpperCamelCase    :      str						     =					self.read_limit - self.n_seen
										_UpperCamelCase    :      Dict						     =					self.f.read(remaining_amount if n < 0 else min(lowerCAmelCase__,				lowerCAmelCase__							)							)
										self.n_seen += len(lowerCAmelCase__							)
										return data






				def __iter__(				self	:					int							)			->   List[Any]:




										'''simple docstring'''



										yield self.read(n=4 * 1_0_2_4 * 1_0_2_4							)






				def __exit__(				self	:					Optional[Any],				*lowerCAmelCase__	:					List[Any]							)			->   Tuple:




										'''simple docstring'''



										self.f.close()




class 				_a		:






				def __init__(				self	:					List[str],				lowerCAmelCase__	:					Optional[Any]							)			->   Union[str, Any]:




										'''simple docstring'''



										_UpperCamelCase    :      Optional[Any]						     =					args






				def        snake_case			(				self	:					List[Any]							)			->   Tuple:




										'''simple docstring'''



										_UpperCamelCase    :      Union[str, Any]						     =					json.loads(sys.stdin.readline().strip()							)
										if not (init_msg.get('''event'''							) == "init" and init_msg.get('''operation'''							) == "upload"):
																write_msg({'''error''': {'''code''': 3_2, '''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:
																_UpperCamelCase    :      int						     =					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							)

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

																_UpperCamelCase    :      str						     =					[]
																for i, presigned_url in enumerate(lowerCAmelCase__							):
																						with FileSlice(lowerCAmelCase__,				seek_from=i * chunk_size,				read_limit=lowerCAmelCase__							) as data:
																												_UpperCamelCase    :      Any						     =					requests.put(lowerCAmelCase__,				data=lowerCAmelCase__							)
																												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.

																_UpperCamelCase    :      str						     =					requests.post(
																    lowerCAmelCase__,				json={
																        '''oid''': oid,
																        '''parts''': parts,
																    },				)
																r.raise_for_status()

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



